title
Statistics for Data Science | Probability and Statistics | Statistics Tutorial | Ph.D. (Stanford)

description
🔥1000+ Free Courses With Free Certificates: https://www.mygreatlearning.com/academy?ambassador_code=GLYT_DES_Vfo5le26IhY&utm_source=GLYT&utm_campaign=GLYT_DES_Vfo5le26IhY 🏁 Watch the video and attend this quiz and get a certificate of completion: https://glacad.me/3Huj9EB Great Learning offers a range of extensive Data Science courses that enable candidates for diverse work professions in Data Science and other trending domains. The faculty team of the Data Science Courses comprises top academicians in Data Science along with many skilled industry practitioners from leading organizations that practice Data Science. Over 500+ Hiring Partners & 8000+ career transitions over varied domains. Know More: https://glacad.me/3whKUsQ More full courses from Dr Sarkar, Ph.D. Stanford: https://www.youtube.com/watch?v=FPM6it4v8MY One of the most critical aspects of the data science approach is our perception of getting the information processed. In developing insights from our accumulated data, we dig out the possibilities. And those possibilities are known as statistical analysis in Data science. Statistics acts as a tool to gather, extract, analyze, and review data, which is an input to Data science techniques; hence, learning statistics is a baby step toward becoming a data scientist. Great Learning‘s Statistics for Data Science course is for beginners and professionals who want to upgrade their skills in data science domains and learn everything about statistical analysis. 🏁 Topics Covered: Introduction - 00:00:00 1. Statistics vs Machine Learning - 00:02:22 2. Types of Statistics [Descriptive, Prescriptive and Predictive - 00:09:05 3. Types of Data - 01:50:45 4. Correlation – 02:46:02 5. Covariance – 02:52:33 6. Introduction to Probability – 04:26:55 7. Conditional Probability with Baye’s Theorem – 05:24:00 8. Binomial Distribution – 06:17:01 9. Poisson Distribution – 06:36:02 🔥Check Our Free Courses on with free certificate: ✔ Statistics for Data Science course: https://glacad.me/3IUE3Np ✔ Introduction to Data Science: https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-data-science?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP22 ✔ Data Science Foundations: https://www.mygreatlearning.com/academy/learn-for-free/courses/data-science-foundations?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP22 ✔ Career in Data Science: https://www.mygreatlearning.com/academy/learn-for-free/courses/career-in-data-science?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP22 ✔ R for Data Science: https://www.mygreatlearning.com/academy/learn-for-free/courses/r-for-data-science?ambassador_code=GLYT_DES_Middle_SEP22&utm_source=GLYT&utm_campaign=GLYT_DES_Middle_SEP22 Here are the links for our other full course videos: Probability and Statistics Full Course: https://www.youtube.com/watch?v=z9siRCCElls Machine Learning with Python: https://www.youtube.com/watch?v=RnFGwxJwx-0&t=287s Hadoop Full Course: https://www.youtube.com/watch?v=JK2MdJAWEGc Time series analysis: https://www.youtube.com/watch?v=FPM6it4v8MY&t=8209s Tableau Training for Beginners: https://www.youtube.com/watch?v=6mBtTNggkUk&t=994s Python for Data Science: https://www.youtube.com/watch?v=edvg4eHi_Mw&t=17669s Artificial Intelligence Tutorial: https://www.youtube.com/watch?v=opgTF9Yf3Dk&t=729s ⚡ About Great Learning Academy: Visit Great Learning Academy to get access to 1000+ free courses with free certificate on Data Science, Data Analytics, Digital Marketing, Artificial Intelligence, Big Data, Cloud, Management, Cybersecurity, Software Development, and many more. These are supplemented with free projects, assignments, datasets, quizzes. You can earn a certificate of completion at the end of the course for free. https://www.mygreatlearning.com/acade ABOUT GL: ⚡ About Great Learning: With more than 5.4 Million+ learners in 170+ countries, Great Learning, a part of the BYJU'S group, is a leading global edtech company for professional and higher education offering industry-relevant programs in the blended, classroom, and purely online modes across technology, data and business domains. These programs are developed in collaboration with the top institutions like Stanford Executive Education, MIT Professional Education, The University of Texas at Austin, NUS, IIT Madras, IIT Bombay & more. SOCIAL MEDIA LINKS: 🔹 For more interesting tutorials, don't forget to subscribe to our channel: https://glacad.me/YTsubscribe 🔹 For more updates on courses and tips follow us on: ✅ Telegram: https://t.me/GreatLearningAcademy ✅ Facebook: https://www.facebook.com/GreatLearningOfficial/ ✅ LinkedIn: https://www.linkedin.com/school/great-learning/mycompany/verification/ ✅ Follow our Blog: https://glacad.me/GL_Blog #datascience #datasciencetutorial #statistics

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{'title': 'Statistics for Data Science | Probability and Statistics | Statistics Tutorial | Ph.D. (Stanford)', 'heatmap': [{'end': 3122.572, 'start': 2598.134, 'weight': 0.728}, {'end': 4160.312, 'start': 3894.407, 'weight': 1}], 'summary': 'Tutorial series by dr. abhinanda sarkar, a stanford university phd holder, covers statistics, data analysis, visualization, probability, and various statistical analysis techniques in data science. it emphasizes the practical applications of these concepts in fields like product sales analysis and gene expression levels and provides real-world examples to illustrate key probability fundamentals and distributions.', 'chapters': [{'end': 345.009, 'segs': [{'end': 52.31, 'src': 'embed', 'start': 22.358, 'weight': 0, 'content': [{'end': 24.019, 'text': 'So, with the help of statistics,', 'start': 22.358, 'duration': 1.661}, {'end': 33.102, 'text': 'you can make predictions such as New York will be hit with multiple tornadoes at the end of this month or the stock market is going to crash by this weekend.', 'start': 24.019, 'duration': 9.083}, {'end': 38.585, 'text': "Now, all of this sounds magical, doesn't it? Well, to be honest, it's just statistics and not magic.", 'start': 33.563, 'duration': 5.022}, {'end': 41.286, 'text': "And you don't really need a crystal ball to see into the future.", 'start': 38.885, 'duration': 2.401}, {'end': 48.009, 'text': 'So keeping the importance of statistics in mind, we have come up with this comprehensive course by Dr. Abhinanda Sarkar.', 'start': 41.826, 'duration': 6.183}, {'end': 52.31, 'text': 'Dr. Abhinanda Sarkar has his PhD in statistics from Stanford University.', 'start': 48.469, 'duration': 3.841}], 'summary': 'Statistics can help make predictions, like multiple tornadoes in new york or stock market crash, and dr. abhinanda sarkar offers a comprehensive statistics course.', 'duration': 29.952, 'max_score': 22.358, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY22358.jpg'}, {'end': 248.614, 'src': 'embed', 'start': 219.515, 'weight': 4, 'content': [{'end': 223.318, 'text': "so there's a difference in the way these two communities approach things.", 'start': 219.515, 'duration': 3.803}, {'end': 227.82, 'text': 'my job is not to resolve that.', 'start': 226.519, 'duration': 1.301}, {'end': 237.186, 'text': "because in the world that you will face you see a lot more of this kind of thinking than you'll see in this thing.", 'start': 229.841, 'duration': 7.345}, {'end': 244.692, 'text': 'because in this world the data is cheap and the question is expensive.', 'start': 239.988, 'duration': 4.704}, {'end': 248.614, 'text': "and you're paid for asking the right question.", 'start': 246.833, 'duration': 1.781}], 'summary': 'Communities have different approaches. data is cheap, questions are expensive.', 'duration': 29.099, 'max_score': 219.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY219515.jpg'}, {'end': 354.277, 'src': 'embed', 'start': 324.992, 'weight': 3, 'content': [{'end': 325.873, 'text': 'she came back from delhi.', 'start': 324.992, 'duration': 0.881}, {'end': 326.894, 'text': 'she came back with two of these.', 'start': 325.893, 'duration': 1.001}, {'end': 327.975, 'text': "i don't know where she picked them up.", 'start': 326.974, 'duration': 1.001}, {'end': 329.817, 'text': 'so my daughter.', 'start': 329.116, 'duration': 0.701}, {'end': 336.583, 'text': 'the first thing she did, she took one of this, and she took this thing out because she thought of the whole wristband as an unnecessary idea.', 'start': 329.817, 'duration': 6.766}, {'end': 340.385, 'text': "that didn't occur to her.", 'start': 339.144, 'duration': 1.241}, {'end': 341.506, 'text': "i mean, that's a separate thing.", 'start': 340.405, 'duration': 1.101}, {'end': 345.009, 'text': "that's a nice little beautiful red wristband, etc.", 'start': 341.946, 'duration': 3.063}, {'end': 346.43, 'text': 'so watch is different thing.', 'start': 345.509, 'duration': 0.921}, {'end': 347.691, 'text': "but let's say that you're a watch company.", 'start': 346.45, 'duration': 1.241}, {'end': 350.274, 'text': "nobody's buying your watches or fewer people are buying your watches.", 'start': 347.751, 'duration': 2.523}, {'end': 354.277, 'text': 'now, how are you going to solve this problem, or how are you going to process this information??', 'start': 350.774, 'duration': 3.503}], 'summary': 'Daughter removed wristband from watch, poses challenge for watch company.', 'duration': 29.285, 'max_score': 324.992, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY324992.jpg'}], 'start': 0.209, 'title': 'Statistics in data science', 'summary': "Discusses the importance of statistics in data science, emphasizing its role in making predictions and highlighting the value of a comprehensive course by dr. abhinanda sarkar, a stanford university phd holder and analytics expert, presented by great learning's business analytics and business intelligence course. it also covers the difference between statistics and machine learning, various types of statistics, probability, and the importance of asking the right question in data analysis for effective problem-solving.", 'chapters': [{'end': 98.591, 'start': 0.209, 'title': 'Data science: statistics and predictions', 'summary': "Discusses the importance of statistics in data science, highlighting its role in making predictions and emphasizes the value of a comprehensive course by dr. abhinanda sarkar, a stanford university phd holder and analytics expert, presented by great learning's business analytics and business intelligence course.", 'duration': 98.382, 'highlights': ['Dr. Abhinanda Sarkar, a Stanford University PhD holder, presents a comprehensive course in statistics for data science, emphasizing its importance in making predictions.', 'The average salary for data science and machine learning jobs is $120,000 per year, making it one of the top five jobs globally, according to LinkedIn.', "Great Learning's Business Analytics and Business Intelligence course, featuring Dr. Abhinanda Sarkar's session, has been ranked as the number one analytics program for the past four years.", "The tutorial by Dr. Abhinanda Sarkar will be available on Great Learning's YouTube channel for a limited period to provide access to high-quality content for learners worldwide."]}, {'end': 345.009, 'start': 99.232, 'title': 'Statistics vs. machine learning', 'summary': 'Covers the difference between statistics and machine learning, various types of statistics, probability, and the value of asking the right question in data analysis, highlighting the importance of understanding the data and formulating the right questions for effective problem-solving.', 'duration': 245.777, 'highlights': ['The value of asking the right question in data analysis In the world of statistics, the question is cheap and the data is expensive, highlighting the importance of asking the right questions for effective problem-solving.', 'Difference between statistics and machine learning Statistics focuses on formulating a problem and then collecting data to solve it, whereas machine learning starts with the data and aims to interpret it, showcasing the varying approaches of the two communities.', 'Understanding the concept of correlation and covariance The chapter comprehensively explains the concept of correlation and covariance, essential statistical measures for analyzing relationships and variability in data.']}], 'duration': 344.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY209.jpg', 'highlights': ["Great Learning's Business Analytics and Business Intelligence course, featuring Dr. Abhinanda Sarkar's session, has been ranked as the number one analytics program for the past four years.", 'The average salary for data science and machine learning jobs is $120,000 per year, making it one of the top five jobs globally, according to LinkedIn.', 'Dr. Abhinanda Sarkar, a Stanford University PhD holder, presents a comprehensive course in statistics for data science, emphasizing its importance in making predictions.', 'The value of asking the right question in data analysis In the world of statistics, the question is cheap and the data is expensive, highlighting the importance of asking the right questions for effective problem-solving.', 'Difference between statistics and machine learning Statistics focuses on formulating a problem and then collecting data to solve it, whereas machine learning starts with the data and aims to interpret it, showcasing the varying approaches of the two communities.', 'Understanding the concept of correlation and covariance The chapter comprehensively explains the concept of correlation and covariance, essential statistical measures for analyzing relationships and variability in data.', "The tutorial by Dr. Abhinanda Sarkar will be available on Great Learning's YouTube channel for a limited period to provide access to high-quality content for learners worldwide."]}, {'end': 2584.656, 'segs': [{'end': 561.174, 'src': 'embed', 'start': 535.226, 'weight': 6, 'content': [{'end': 541.709, 'text': 'you usually follow something like a three-step process, and you may have seen this, and this covers both these sites,', 'start': 535.226, 'duration': 6.483}, {'end': 544.471, 'text': 'and these words should be should be familiar to to some extent.', 'start': 541.709, 'duration': 2.762}, {'end': 546.352, 'text': 'the first is called descriptive.', 'start': 545.011, 'duration': 1.341}, {'end': 549.754, 'text': 'the second is called predictive.', 'start': 548.413, 'duration': 1.341}, {'end': 561.174, 'text': "and the third is called prescriptive have these words been introduced you at least in this call at least you've read it.", 'start': 552.708, 'duration': 8.466}], 'summary': 'Three-step process: descriptive, predictive, prescriptive for site data analysis.', 'duration': 25.948, 'max_score': 535.226, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY535226.jpg'}, {'end': 608.598, 'src': 'embed', 'start': 579.848, 'weight': 1, 'content': [{'end': 587.499, 'text': "the descriptive problem is a problem that says that this try for me where and i'm losing my sales and when i'm losing my sense.", 'start': 579.848, 'duration': 7.651}, {'end': 590.782, 'text': 'it just describes the problem for me.', 'start': 589.2, 'duration': 1.582}, {'end': 594.005, 'text': 'it tells me where the problem is it locates it isolates it.', 'start': 590.882, 'duration': 3.123}, {'end': 604.334, 'text': 'the predictive problem says look at this data and give me an idea as to what might happen.', 'start': 597.187, 'duration': 7.147}, {'end': 608.598, 'text': 'or what would happen if i change this that or the other.', 'start': 606.115, 'duration': 2.483}], 'summary': 'Descriptive problem locates issues, predictive problem forecasts outcomes based on data.', 'duration': 28.75, 'max_score': 579.848, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY579848.jpg'}, {'end': 682.604, 'src': 'embed', 'start': 653.242, 'weight': 3, 'content': [{'end': 656.703, 'text': "i'm trying to see if something happens to let's say one part of my data.", 'start': 653.242, 'duration': 3.461}, {'end': 665.585, 'text': 'what will happen to the other part of my data and then based on that the doctor carries out a predictive analysis of you because i see this.', 'start': 656.723, 'duration': 8.862}, {'end': 667.965, 'text': 'i now think you have this issue.', 'start': 666.405, 'duration': 1.56}, {'end': 671.226, 'text': 'you have this thing going on.', 'start': 670.106, 'duration': 1.12}, {'end': 674.186, 'text': "let's say i'm diagnosing you as being pre-diabetic.", 'start': 671.246, 'duration': 2.94}, {'end': 677.687, 'text': "you're not yet diabetic, but you're happily on the way to becoming a diabetic.", 'start': 674.206, 'duration': 3.481}, {'end': 682.604, 'text': 'now because of this i now have to issue your prescription.', 'start': 680.063, 'duration': 2.541}], 'summary': 'Analysis of data helps predict pre-diabetic condition and issue prescriptions.', 'duration': 29.362, 'max_score': 653.242, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY653242.jpg'}, {'end': 747.715, 'src': 'embed', 'start': 723.94, 'weight': 4, 'content': [{'end': 731.603, 'text': 'optimize his or her welfare? by making sure that i control the blood sugar the best and that i postpone the onset of diabetes as best as i can.', 'start': 723.94, 'duration': 7.663}, {'end': 737.11, 'text': "it's a complex optimization problem of some sort in a business also.", 'start': 733.208, 'duration': 3.902}, {'end': 738.771, 'text': "it's a complex optimization problem.", 'start': 737.15, 'duration': 1.621}, {'end': 745.354, 'text': 'i need to be able to sell more watches, but i also need to be able to make money doing so.', 'start': 740.311, 'duration': 5.043}, {'end': 747.715, 'text': 'i can increase my sales.', 'start': 746.634, 'duration': 1.081}], 'summary': 'The speaker aims to control blood sugar to delay diabetes onset and optimize sales of watches.', 'duration': 23.775, 'max_score': 723.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY723940.jpg'}, {'end': 1588.449, 'src': 'embed', 'start': 1561.035, 'weight': 8, 'content': [{'end': 1564.537, 'text': 'that helps me get to conclusions of this kind a little more rigorously.', 'start': 1561.035, 'duration': 3.502}, {'end': 1571.18, 'text': 'now to be able to quantify what these plus minuses are is going to take a.', 'start': 1566.058, 'duration': 5.122}, {'end': 1573.461, 'text': 'take us a little bit of time and we will not get that.', 'start': 1571.18, 'duration': 2.281}, {'end': 1575.582, 'text': 'this residency will get their next residency.', 'start': 1573.461, 'duration': 2.121}, {'end': 1583.026, 'text': 'to say that in order to in order to say i saw 135 or 135 plus minus something that question now needs to be answered.', 'start': 1577.303, 'duration': 5.723}, {'end': 1588.449, 'text': 'but to do that i need to have two particular instruments at my disposal.', 'start': 1584.687, 'duration': 3.762}], 'summary': 'Quantifying the plus-minus conclusions will take time, requiring two specific instruments.', 'duration': 27.414, 'max_score': 1561.035, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY1561035.jpg'}, {'end': 2125.146, 'src': 'embed', 'start': 2103.195, 'weight': 7, 'content': [{'end': 2112.82, 'text': 'but more often than not, what you see is that when you, when you, when, when jupiter sees it, it will see an any xls file as a csv file.', 'start': 2103.195, 'duration': 9.625}, {'end': 2114.441, 'text': 'or go and make the change yourself.', 'start': 2112.82, 'duration': 1.621}, {'end': 2119.704, 'text': 'or you can have other xls other restatements in it as well.', 'start': 2116.502, 'duration': 3.202}, {'end': 2125.146, 'text': 'you can change functions inside it and you can figure out how much to head what this tells you is the head and the tail of the data.', 'start': 2120.184, 'duration': 4.962}], 'summary': 'Jupiter often sees xls files as csv, allowing changes and data analysis.', 'duration': 21.951, 'max_score': 2103.195, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY2103195.jpg'}, {'end': 2224.708, 'src': 'embed', 'start': 2178.767, 'weight': 0, 'content': [{'end': 2181.65, 'text': 'if a data frame is created when it gets created.', 'start': 2178.767, 'duration': 2.883}, {'end': 2188.739, 'text': 'the software knows as to whether it is talking about a number or whether it is talking about categories.', 'start': 2183.813, 'duration': 4.926}, {'end': 2203.059, 'text': 'there are certain challenges to that.', 'start': 2201.698, 'duration': 1.361}, {'end': 2205.8, 'text': 'you can see one particular challenge to this.', 'start': 2203.939, 'duration': 1.861}, {'end': 2224.708, 'text': 'what does this 180 mean? counts why do you think there are so many decimal places that comes here? 14 years of experience 16 years of experience.', 'start': 2212.803, 'duration': 11.905}], 'summary': 'Challenges in categorizing data, including numerical and categorical distinctions, with examples of 180 counts and years of experience.', 'duration': 45.941, 'max_score': 2178.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY2178767.jpg'}], 'start': 345.509, 'title': 'Data analysis and visualization', 'summary': 'Covers various aspects including analyzing sales data for a watch company, challenges in building autonomous vehicles, variability in blood content, understanding customer characteristics, and data analysis and visualization with python.', 'chapters': [{'end': 783.19, 'start': 345.509, 'title': 'Analyzing sales data for watch company', 'summary': 'Discusses the process of analyzing sales data for a watch company to identify reasons for declining sales, potential solutions, and the three-step process of descriptive, predictive, and prescriptive analysis.', 'duration': 437.681, 'highlights': ['The three-step process of descriptive, predictive, and prescriptive analysis is crucial in understanding and addressing the declining sales of the watch company. The three-step process of descriptive, predictive, and prescriptive analysis is crucial in understanding and addressing the declining sales of the watch company. Descriptive analysis helps in locating and isolating the problem, predictive analysis explores potential outcomes based on changes, and prescriptive analysis involves translating the insights into actionable strategies.', 'Analyzing sales data to identify segments with declining sales and understanding the reasons behind the sales decline is essential for making informed business decisions. Analyzing sales data to identify segments with declining sales and understanding the reasons behind the sales decline is essential for making informed business decisions. This involves asking questions about the sales trends, customer segments, and the impact of pricing strategies to address the declining sales.', 'The approach of transforming watches into luxury items and understanding the potential impact on sales can be a part of the predictive analysis to explore different strategies for increasing sales. The approach of transforming watches into luxury items and understanding the potential impact on sales can be a part of the predictive analysis to explore different strategies for increasing sales. This involves predicting the response of customers to changes in pricing and branding strategies to make watches aspirational and fashion statements.']}, {'end': 1086.095, 'start': 784.45, 'title': 'Challenges in building autonomous vehicles', 'summary': 'Discusses the challenges in building autonomous vehicles, including the need to follow road rules and make decisions based on various constraints and data, highlighting the complexities of descriptive analytics in making informed decisions based on a vast amount of data.', 'duration': 301.645, 'highlights': ['The challenges in building autonomous vehicles include the need to follow road rules and make decisions based on various constraints. Autonomous vehicles need to adhere to road rules and make decisions based on constraints, such as avoiding sudden stops when encountering pedestrians.', 'Descriptive analytics involves making informed decisions based on a vast amount of data, such as in the case of recommending specific blood tests based on symptoms. Descriptive analytics requires making informed decisions based on a vast amount of data, as exemplified by the challenge of recommending specific blood tests based on symptoms, highlighting the complexities of informed decision-making.', 'The complexities of descriptive analytics are evident in making informed decisions based on a vast amount of data, such as in the example of recommending specific blood tests based on symptoms. The complexities of descriptive analytics are evident in making informed decisions based on a vast amount of data, as exemplified by the challenge of recommending specific blood tests based on symptoms, highlighting the complexities of informed decision-making.']}, {'end': 1732.469, 'start': 1087.816, 'title': 'Variability in blood content', 'summary': 'Discusses the variability in blood content, highlighting the random nature of bodily fluids and the challenges in accurately measuring and interpreting blood data, emphasizing the need for descriptive analytics and probabilistic language to address this variability.', 'duration': 644.653, 'highlights': ['The random nature of bodily fluids poses challenges in accurately measuring and interpreting blood data, necessitating the use of descriptive analytics and probabilistic language. Random variability in blood content, challenges in measuring and interpreting blood data, need for descriptive analytics and probabilistic language.', 'Blood content varies not only over time but also between different parts of the body, leading to complexities in sampling and interpretation. Variability in blood content over time and between different body parts, complexities in sampling and interpretation.', 'The need for descriptive analytics and probabilistic language to understand and quantify the variability in blood content, with a focus on creating mathematical statements based on descriptive data. Importance of descriptive analytics and probabilistic language, creating mathematical statements based on descriptive data.']}, {'end': 2000.417, 'start': 1733.55, 'title': 'Understanding customer characteristics', 'summary': 'Discusses the statistical approach to understanding customer characteristics through collecting and analyzing data on treadmill purchases, focusing on demographics, usage frequency, and fitness levels, along with the implications for market research and product development.', 'duration': 266.867, 'highlights': ['The chapter discusses the statistical approach to understanding customer characteristics through collecting and analyzing data on treadmill purchases. This involves investigating differences across product lines with respect to customer characteristics, collecting data on individuals who purchased treadmills in the past three months, and considering demographics, usage frequency, and fitness levels.', 'The implications for market research and product development are emphasized, including the concept of product-market fit. The discussion touches on the importance of matching what can be made with what people will buy, highlighting the significance of product-market fit in physical and software product spaces.', 'The significance of understanding customer characteristics from both marketing and product engineering perspectives is highlighted. The approach delves into understanding customer characteristics from a marketing point of view (who buys) and a product engineering perspective (what sells, what kind of product should be made), providing insights for entrepreneurs and business decision-making.']}, {'end': 2584.656, 'start': 2002.698, 'title': 'Data analysis and visualization with python', 'summary': 'Discusses the use of python libraries like pandas, numpy, and seaborn for data analysis and visualization, addressing challenges with data types and providing insights on representing variables with one representative number.', 'duration': 581.958, 'highlights': ['The use of Python libraries like Pandas, NumPy, and Seaborn for data analysis and visualization', 'Challenges with data types and representation of variables with one representative number', 'Insights on over-engineering products based on weight considerations']}], 'duration': 2239.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY345509.jpg', 'highlights': ['Descriptive, predictive, and prescriptive analysis is crucial for understanding and addressing declining sales.', 'Analyzing sales data to identify segments with declining sales is essential for making informed business decisions.', 'Transforming watches into luxury items can be part of predictive analysis to explore different strategies for increasing sales.', 'Challenges in building autonomous vehicles include following road rules and making decisions based on various constraints.', 'Descriptive analytics involves making informed decisions based on a vast amount of data, exemplified by recommending specific blood tests based on symptoms.', 'Random variability in blood content poses challenges in accurately measuring and interpreting blood data, necessitating descriptive analytics and probabilistic language.', 'Understanding customer characteristics involves investigating differences across product lines, collecting data on individuals who purchased treadmills, and considering demographics, usage frequency, and fitness levels.', 'The implications for market research and product development are emphasized, including the concept of product-market fit.', 'The significance of understanding customer characteristics from both marketing and product engineering perspectives is highlighted.', 'The use of Python libraries like Pandas, NumPy, and Seaborn for data analysis and visualization is discussed.']}, {'end': 3659.931, 'segs': [{'end': 3122.572, 'src': 'heatmap', 'start': 2598.134, 'weight': 0.728, 'content': [{'end': 2599.735, 'text': 'this is what is called a five-point summary.', 'start': 2598.134, 'duration': 1.601}, {'end': 2610.921, 'text': 'i report out the minimum the 25% point the 50% point the 75% point and the maximum variable by variable.', 'start': 2600.996, 'duration': 9.925}, {'end': 2612.502, 'text': 'i report five numbers.', 'start': 2610.941, 'duration': 1.561}, {'end': 2628.151, 'text': 'i report the lowest what is 25% mean? 25% of my data set or the people are younger than 24.', 'start': 2615.003, 'duration': 13.148}, {'end': 2632.254, 'text': 'the youngest is 80, 25% or a quarter of them are between 18 and 24, a quarter between 24 and 26,', 'start': 2628.151, 'duration': 4.103}, {'end': 2647.045, 'text': 'a quarter between 26 and 33 and a quarter are between 33 and 50..', 'start': 2632.254, 'duration': 14.791}, {'end': 2648.847, 'text': 'this is what is known as a distribution.', 'start': 2647.045, 'duration': 1.802}, {'end': 2652.229, 'text': 'this is what is known as a distribution statisticians love distributions.', 'start': 2649.387, 'duration': 2.842}, {'end': 2655.913, 'text': 'they capture the variability in the data and they would do all kinds of things with it.', 'start': 2652.29, 'duration': 3.623}, {'end': 2658.935, 'text': "so i'm going to drop typical shape of a distribution.", 'start': 2656.573, 'duration': 2.362}, {'end': 2662.098, 'text': 'we will make more sense of it later on.', 'start': 2660.176, 'duration': 1.922}, {'end': 2664.66, 'text': 'this is a theoretical distribution distribution.', 'start': 2662.658, 'duration': 2.002}, {'end': 2666.582, 'text': "for example, let's say has a minimum.", 'start': 2664.7, 'duration': 1.882}, {'end': 2671.146, 'text': 'as a maximum, as a 25% point.', 'start': 2668.844, 'duration': 2.302}, {'end': 2678.82, 'text': 'as a 50% point, it says 75%.', 'start': 2671.146, 'duration': 7.674}, {'end': 2684.183, 'text': 'in terms of probabilities this 25% here.', 'start': 2678.82, 'duration': 5.363}, {'end': 2686.165, 'text': '25% here.', 'start': 2684.183, 'duration': 1.982}, {'end': 2687.746, 'text': '25% here.', 'start': 2686.165, 'duration': 1.581}, {'end': 2690.908, 'text': '25% here, if you want to think in terms of pure description.', 'start': 2687.746, 'duration': 3.162}, {'end': 2692.829, 'text': 'this is not a probability is just a proportion.', 'start': 2691.228, 'duration': 1.601}, {'end': 2703.156, 'text': 'if you want to think in terms of probabilities, what this means is that out of 180 people out of 180 people if i draw one person at random.', 'start': 2694.83, 'duration': 8.326}, {'end': 2712.592, 'text': "if i draw one person at random, there's a 25% chance that that person's weight is going to be below below 24..", 'start': 2704.966, 'duration': 7.626}, {'end': 2715.835, 'text': 'age 24 correct.', 'start': 2712.592, 'duration': 3.243}, {'end': 2718.176, 'text': "if you want to think in terms of probabilities, we'll do that tomorrow.", 'start': 2715.915, 'duration': 2.261}, {'end': 2720.899, 'text': 'but this is a description.', 'start': 2719.938, 'duration': 0.961}, {'end': 2731.447, 'text': 'so what this description does is it gives you an idea as to what value to use in which situation.', 'start': 2722.42, 'duration': 9.027}, {'end': 2740.444, 'text': "so for example, you could say that i'm going to use 2026 as my representative age.", 'start': 2732.308, 'duration': 8.136}, {'end': 2754.107, 'text': "if i do that what is the logic i'm using? this this 25% this 50% point so to speak this is called the median.", 'start': 2743.105, 'duration': 11.002}, {'end': 2765.767, 'text': "this is called the median and we'll see it median means age of the average person.", 'start': 2757.628, 'duration': 8.139}, {'end': 2770.61, 'text': 'first slot.', 'start': 2769.729, 'duration': 0.881}, {'end': 2786.758, 'text': 'take the middle person and ask how old are you? the age of the average person i could also ask for the average age of the person.', 'start': 2772.131, 'duration': 14.627}, {'end': 2796.409, 'text': 'which is what which is the mean? which is 1 over n.', 'start': 2789.08, 'duration': 7.329}, {'end': 2798.45, 'text': 'x1 plus xn.', 'start': 2796.409, 'duration': 2.041}, {'end': 2800.311, 'text': 'now this is algebra.', 'start': 2799.071, 'duration': 1.24}, {'end': 2807.656, 'text': 'what you have to do is you have to put n equal to 180.', 'start': 2802.613, 'duration': 5.043}, {'end': 2811.178, 'text': 'this is the first stage the second age the third age up to 180.', 'start': 2807.656, 'duration': 3.522}, {'end': 2812.339, 'text': '1 by 180.', 'start': 2811.178, 'duration': 1.161}, {'end': 2812.459, 'text': 'age 1.', 'start': 2812.339, 'duration': 0.12}, {'end': 2822.793, 'text': 'plus age 180.', 'start': 2812.459, 'duration': 10.334}, {'end': 2836.844, 'text': 'this is called the mean this value is what? 28.79 the average age is about 28 years or 28 and a half years 28.8 years.', 'start': 2822.793, 'duration': 14.051}, {'end': 2841.627, 'text': 'but the age of the average person is 26.', 'start': 2838.005, 'duration': 3.622}, {'end': 2847.372, 'text': 'yes the difference between the two.', 'start': 2841.627, 'duration': 5.745}, {'end': 2856.477, 'text': 'what is the age of the person? so i described the median as the age of the average person.', 'start': 2848.793, 'duration': 7.684}, {'end': 2861.419, 'text': 'and i described the mean as the average age of a person.', 'start': 2858.338, 'duration': 3.081}, {'end': 2867.121, 'text': "now he's looking at me like saying you have to be kidding me.", 'start': 2864.901, 'duration': 2.22}, {'end': 2869.222, 'text': "that's confusing.", 'start': 2868.482, 'duration': 0.74}, {'end': 2874.725, 'text': 'i admit to it the easy way to understand it could be this.', 'start': 2871.623, 'duration': 3.102}, {'end': 2877.046, 'text': 'what is the mean?', 'start': 2875.745, 'duration': 1.301}, {'end': 2879.447, 'text': 'add them all up, divided by how many there are?', 'start': 2877.046, 'duration': 2.401}, {'end': 2881.661, 'text': 'what is the median?', 'start': 2880.54, 'duration': 1.121}, {'end': 2885.102, 'text': 'shot them from the smallest to the largest.', 'start': 2881.661, 'duration': 3.441}, {'end': 2885.802, 'text': 'pick off the middle?', 'start': 2885.102, 'duration': 0.7}, {'end': 2889.984, 'text': 'if there are any even number, what do you do??', 'start': 2888.143, 'duration': 1.841}, {'end': 2893.386, 'text': 'you take the average of the two middle ones.', 'start': 2890.945, 'duration': 2.441}, {'end': 2896.587, 'text': "if they're the same, it will be the same number.", 'start': 2894.706, 'duration': 1.881}, {'end': 2898.808, 'text': "if they're not, it will be a number between them.", 'start': 2896.927, 'duration': 1.881}, {'end': 2903.25, 'text': 'so sometimes the median may show up with a point five or something like that.', 'start': 2900.269, 'duration': 2.981}, {'end': 2909.293, 'text': "for that reason, if there isn't integer counts but there are an even number of cups.", 'start': 2903.25, 'duration': 6.043}, {'end': 2919.761, 'text': "now, which do you think is better? you're giving the right answer.", 'start': 2912.657, 'duration': 7.104}, {'end': 2920.982, 'text': 'it depends.', 'start': 2920.102, 'duration': 0.88}, {'end': 2922.983, 'text': "you'll figure out that i like that answer.", 'start': 2921.583, 'duration': 1.4}, {'end': 2926.486, 'text': 'they both make sense.', 'start': 2925.705, 'duration': 0.781}, {'end': 2928.347, 'text': 'they both make sense.', 'start': 2927.586, 'duration': 0.761}, {'end': 2933.77, 'text': "it depends on what what context you're going to use it for in certain case.", 'start': 2928.387, 'duration': 5.383}, {'end': 2942.454, 'text': 'yes is the age of the average person is reading from the average person.', 'start': 2933.79, 'duration': 8.664}, {'end': 2951.497, 'text': "okay, if you're talking in terms of parameters, so use an interesting term.", 'start': 2943.214, 'duration': 8.283}, {'end': 2958.739, 'text': "he's saying what is the parameter? i'm after parameter is an interesting word parameter refers to something or in general in a population.", 'start': 2951.737, 'duration': 7.002}, {'end': 2963.32, 'text': "it's an unknown thing that i'm trying to get after for example, blood sugar is a parameter.", 'start': 2958.799, 'duration': 4.521}, {'end': 2966.901, 'text': "it exists, but i don't know it.", 'start': 2964.6, 'duration': 2.301}, {'end': 2969.45, 'text': "i'm trying to get my handle on it.", 'start': 2968.089, 'duration': 1.361}, {'end': 2978.175, 'text': "correct so if i'm thinking in terms of parameters then these are different parameters.", 'start': 2970.991, 'duration': 7.184}, {'end': 2981.197, 'text': "so let's let's look at a distribution here.", 'start': 2979.256, 'duration': 1.941}, {'end': 2982.718, 'text': "i'm not sure whether this will pick up things.", 'start': 2981.257, 'duration': 1.461}, {'end': 2983.358, 'text': 'i hope so.', 'start': 2982.958, 'duration': 0.4}, {'end': 2994.185, 'text': 'so the median is that is the median is a parameter such that on this side.', 'start': 2986.28, 'duration': 7.905}, {'end': 3002.064, 'text': 'i have 50% and on this side, i have 50% this is the median.', 'start': 2994.285, 'duration': 7.779}, {'end': 3009.71, 'text': 'the mean is what is called the first moment.', 'start': 3006.007, 'duration': 3.703}, {'end': 3013.953, 'text': 'what that means is think of this as a plate of metal.', 'start': 3010.691, 'duration': 3.262}, {'end': 3017.076, 'text': 'and i want to balance it on something.', 'start': 3015.515, 'duration': 1.561}, {'end': 3024.001, 'text': 'where do i put my finger so that it balances it is the cg of the data the center of gravity of the data.', 'start': 3018.076, 'duration': 5.925}, {'end': 3029.577, 'text': 'you can understand the difference between these two now.', 'start': 3026.596, 'duration': 2.981}, {'end': 3034.819, 'text': 'if, for example, i push the data out to the right, what happens to the median?', 'start': 3029.577, 'duration': 5.242}, {'end': 3037.86, 'text': 'nothing happens to the median because the 50-50 split remains the same.', 'start': 3034.819, 'duration': 3.041}, {'end': 3042.001, 'text': 'but if i push the data out to the right, the mean will change.', 'start': 3038.64, 'duration': 3.361}, {'end': 3043.082, 'text': 'it will move to the right.', 'start': 3042.001, 'duration': 1.081}, {'end': 3046.203, 'text': 'your lever, the lever principle right?', 'start': 3043.082, 'duration': 3.121}, {'end': 3050.024, 'text': "if there's more weight on one side, i have to move my finger in order to counterbalance that weight.", 'start': 3046.303, 'duration': 3.721}, {'end': 3052.725, 'text': 'so these are two different parameters.', 'start': 3051.044, 'duration': 1.681}, {'end': 3063.925, 'text': 'if the distribution, for example, is what is called symmetric symmetric means it looks the same on the left as on the right then these two will equal,', 'start': 3053.698, 'duration': 10.227}, {'end': 3071.37, 'text': 'because the idea of going half to the left and half to the right will be the same as the idea of where do i balance because the left is equal to the right?', 'start': 3063.925, 'duration': 7.445}, {'end': 3077.615, 'text': "so when the mean is not equal to the median, that's a signal that the left is not equal to the right.", 'start': 3073.192, 'duration': 4.423}, {'end': 3085.473, 'text': 'and when the mean is a little more than the median, it says that there is some data that has been pushed to the right.', 'start': 3079.588, 'duration': 5.885}, {'end': 3093.681, 'text': 'and that should be something that you can guess here because the mean and the median to some extent are what 2426 etc.', 'start': 3087.656, 'duration': 6.025}, {'end': 3095.062, 'text': 'the lowest is 18.', 'start': 3094.382, 'duration': 0.68}, {'end': 3099.446, 'text': "that's about six six years eight years less than that.", 'start': 3095.062, 'duration': 4.384}, {'end': 3105.232, 'text': "but what is the maximum? 50 that's 25 years beyond the data is pushed to the right a little bit.", 'start': 3099.466, 'duration': 5.766}, {'end': 3109.644, 'text': 'is racing push to the right right technical term is right skewed.', 'start': 3107.062, 'duration': 2.582}, {'end': 3122.572, 'text': 'there are there are shall i say people are more not average on the on the older side than on the younger side.', 'start': 3113.846, 'duration': 8.726}], 'summary': 'The transcript explains the five-point summary, distribution, mean, and median, using an example of age distribution with 180 people.', 'duration': 524.438, 'max_score': 2598.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY2598134.jpg'}, {'end': 2690.908, 'src': 'embed', 'start': 2656.573, 'weight': 0, 'content': [{'end': 2658.935, 'text': "so i'm going to drop typical shape of a distribution.", 'start': 2656.573, 'duration': 2.362}, {'end': 2662.098, 'text': 'we will make more sense of it later on.', 'start': 2660.176, 'duration': 1.922}, {'end': 2664.66, 'text': 'this is a theoretical distribution distribution.', 'start': 2662.658, 'duration': 2.002}, {'end': 2666.582, 'text': "for example, let's say has a minimum.", 'start': 2664.7, 'duration': 1.882}, {'end': 2671.146, 'text': 'as a maximum, as a 25% point.', 'start': 2668.844, 'duration': 2.302}, {'end': 2678.82, 'text': 'as a 50% point, it says 75%.', 'start': 2671.146, 'duration': 7.674}, {'end': 2684.183, 'text': 'in terms of probabilities this 25% here.', 'start': 2678.82, 'duration': 5.363}, {'end': 2686.165, 'text': '25% here.', 'start': 2684.183, 'duration': 1.982}, {'end': 2687.746, 'text': '25% here.', 'start': 2686.165, 'duration': 1.581}, {'end': 2690.908, 'text': '25% here, if you want to think in terms of pure description.', 'start': 2687.746, 'duration': 3.162}], 'summary': 'Describes theoretical distribution with key points and quantifiable data.', 'duration': 34.335, 'max_score': 2656.573, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY2656573.jpg'}, {'end': 3477.525, 'src': 'embed', 'start': 3407.14, 'weight': 1, 'content': [{'end': 3408.562, 'text': 'the tables at the back of this book.', 'start': 3407.14, 'duration': 1.422}, {'end': 3414.306, 'text': "which we learn how to use and then i'll try to convince you that you shouldn't use them.", 'start': 3409.903, 'duration': 4.403}, {'end': 3426.597, 'text': "but remember many of these methods are done in ways in which either you don't have access to computers.", 'start': 3416.488, 'duration': 10.109}, {'end': 3431.863, 'text': "or if you do have access to computers, you don't have them.", 'start': 3428.342, 'duration': 3.521}, {'end': 3439.566, 'text': 'shall we say at runtime? in other words, when i want to run the application on i can build a model using a computer, but i can run it within one.', 'start': 3431.923, 'duration': 7.643}, {'end': 3444.928, 'text': 'the runtime environment for statistics is often done when there are no computers around.', 'start': 3441.106, 'duration': 3.822}, {'end': 3451.67, 'text': 'the build environment can include computers, but the runtime environment can a lot of statistics is done under that kind of situation.', 'start': 3446.588, 'duration': 5.082}, {'end': 3457.212, 'text': 'yes, very much so very much.', 'start': 3455.031, 'duration': 2.181}, {'end': 3464.279, 'text': 'okay. so definitions of skewness and things like that do it.', 'start': 3460.757, 'duration': 3.522}, {'end': 3468.381, 'text': 'do it in the way you usually use a book, which means you go to the index and see if the word is there.', 'start': 3464.279, 'duration': 4.102}, {'end': 3471.602, 'text': 'and then you go back and figure it out.', 'start': 3470.282, 'duration': 1.32}, {'end': 3473.383, 'text': 'it will give you some ideas as to how that works.', 'start': 3471.642, 'duration': 1.741}, {'end': 3477.525, 'text': "it's a nice book, is one of the best books that you have in business statistics,", 'start': 3473.623, 'duration': 3.902}], 'summary': 'The transcript discusses using computers for statistics and emphasizes the difference between build and runtime environments.', 'duration': 70.385, 'max_score': 3407.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY3407140.jpg'}], 'start': 2584.756, 'title': 'Data analysis concepts', 'summary': 'Covers data distribution, statistical summary using a five-point summary, median and mean calculation, parameters in data analysis, difference between mean and median, skewed data, calculation of skewness, and the importance of focusing on specific topics in data analysis.', 'chapters': [{'end': 2909.293, 'start': 2584.756, 'title': 'Data distribution and statistical summary', 'summary': 'Discusses the concept of data distribution and statistical summary, demonstrating the use of a five-point summary to represent variability in the data, including the calculation of median and mean for the given dataset.', 'duration': 324.537, 'highlights': ['The chapter introduces the concept of a five-point summary to represent data variability, including the minimum, 25%, 50%, 75%, and maximum values. The five-point summary provides a concise representation of data variability, aiding in understanding the distribution of the dataset.', 'Explanation of the distribution of data, showcasing the age ranges and proportions within the dataset. The distribution of the dataset is illustrated through age ranges and corresponding proportions, demonstrating the variability in the data.', 'Explanation of the difference between median and mean, with the median representing the age of the average person and the mean indicating the average age of a person. The distinction between median and mean is clarified, with the median representing the age of the average person and the mean indicating the average age of a person within the dataset.']}, {'end': 3195.16, 'start': 2912.657, 'title': 'Understanding parameters in data analysis', 'summary': 'Discusses the concept of parameters in data analysis, explaining the difference between mean and median, their relationship to distribution symmetry, and their sensitivity to outliers, emphasizing their role in understanding data distributions and making inferences.', 'duration': 282.503, 'highlights': ['The chapter explains the difference between mean and median, their relationship to distribution symmetry, and their sensitivity to outliers. Difference between mean and median, relationship to distribution symmetry, sensitivity to outliers', 'The concept of parameters in data analysis is discussed, focusing on their role in understanding data distributions and making inferences. Role of parameters in understanding data distributions and making inferences', "The example of mean income or median income is used to illustrate the insensitivity of median to outliers. Illustration of median's insensitivity to outliers using income example"]}, {'end': 3659.931, 'start': 3195.201, 'title': 'Understanding skewed data', 'summary': 'Discusses the concept of skewness in data analysis, highlighting the calculation of skewness, its impact on statistical analysis, and the recommendation of a statistics book for in-depth understanding, emphasizing the importance of focusing on specific topics rather than attempting to cover everything in equal depth.', 'duration': 464.73, 'highlights': ['The concept of skewness in data analysis is explained, with a focus on calculating skewness using measures such as mean minus median, and the impact of skewed data on statistical analysis, emphasizing the importance of understanding the statistical side of the topic.', "The recommendation of a statistics book that provides in-depth understanding of statistical concepts and analysis, distinguishing between books that focus on coding and those that prioritize conceptual understanding, highlighting the importance of comprehending the 'what' and 'why' of statistical analysis.", 'The suggestion to prioritize specific topics and go into depth in understanding them, acknowledging the potential overwhelming nature of learning numerous topics in equal depth and encouraging individuals to focus on areas they are interested in and seek further learning opportunities if needed, emphasizing the accessibility of additional support and resources for in-depth learning.']}], 'duration': 1075.175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY2584756.jpg', 'highlights': ['The five-point summary aids in understanding data variability, providing a concise representation.', 'The distribution of the dataset is illustrated through age ranges and corresponding proportions, demonstrating variability.', 'The distinction between median and mean is clarified, with the median representing the age of the average person and the mean indicating the average age of a person within the dataset.', 'The chapter explains the difference between mean and median, their relationship to distribution symmetry, and their sensitivity to outliers.', 'Parameters in data analysis play a crucial role in understanding data distributions and making inferences.', "Illustration of median's insensitivity to outliers using income example.", 'The concept of skewness in data analysis is explained, emphasizing the importance of understanding the statistical side of the topic.', "The recommendation of a statistics book that provides in-depth understanding of statistical concepts and analysis, highlighting the importance of comprehending the 'what' and 'why' of statistical analysis.", 'Encouraging individuals to focus on areas they are interested in and seek further learning opportunities if needed, emphasizing the accessibility of additional support and resources for in-depth learning.']}, {'end': 5336.275, 'segs': [{'end': 3690.149, 'src': 'embed', 'start': 3659.931, 'weight': 4, 'content': [{'end': 3667.338, 'text': "this summary gave you what's called to five numbers, five numbers that help you describe the data minimum 25, 50, 75 max.", 'start': 3659.931, 'duration': 7.407}, {'end': 3670.721, 'text': 'will see another graphical description of this.', 'start': 3667.338, 'duration': 3.383}, {'end': 3680.511, 'text': 'it also described for you a mean there is also another number here and this is this number is indicated by the letters std.', 'start': 3671.322, 'duration': 9.189}, {'end': 3690.149, 'text': 'std refers to standard deviation yesterday refers to standard deviation.', 'start': 3682.521, 'duration': 7.628}], 'summary': 'Summary of data includes 5 numbers: min, 25th, 50th, 75th, max, and standard deviation (std).', 'duration': 30.218, 'max_score': 3659.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY3659931.jpg'}, {'end': 4160.312, 'src': 'heatmap', 'start': 3894.407, 'weight': 1, 'content': [{'end': 3895.867, 'text': "so let's look at the first number here.", 'start': 3894.407, 'duration': 1.46}, {'end': 3900.348, 'text': 'so if i look at the head command here, when i did the head command here, what did the head??', 'start': 3896.287, 'duration': 4.061}, {'end': 3901.228, 'text': 'what did the head command??', 'start': 3900.408, 'duration': 0.82}, {'end': 3903.329, 'text': 'give me the first few observations.', 'start': 3901.268, 'duration': 2.061}, {'end': 3904.589, 'text': 'and now this is an 18 year old.', 'start': 3903.349, 'duration': 1.24}, {'end': 3906.59, 'text': 'this probably sorted by age.', 'start': 3905.529, 'duration': 1.061}, {'end': 3907.67, 'text': 'this is an 18 year old correct.', 'start': 3906.63, 'duration': 1.04}, {'end': 3913.485, 'text': "now i'm trying to explain the variability of this data with respect to this 18 year old.", 'start': 3908.803, 'duration': 4.682}, {'end': 3923.89, 'text': 'what is the what is the what why is a variation this 18 number is not the same as 28 and 18 is less than 28.', 'start': 3914.006, 'duration': 9.884}, {'end': 3928.953, 'text': 'so what i want to do is i want to go 18 minus.', 'start': 3923.89, 'duration': 5.063}, {'end': 3933.515, 'text': "28.7 what i'm interested in is this 10.", 'start': 3928.973, 'duration': 4.542}, {'end': 3934.956, 'text': 'this 10 year difference between the two.', 'start': 3933.515, 'duration': 1.441}, {'end': 3947.041, 'text': 'now the person the oldest person in this data set is how old 50 when i get to that rule this 50 will also differ from this 28 by 22 years.', 'start': 3937.128, 'duration': 9.913}, {'end': 3952.949, 'text': "so i'm interested in that 10 and i'm interested in the 22.", 'start': 3948.984, 'duration': 3.965}, {'end': 3958.211, 'text': "i'm not interested in the minus 10 or a minus 22.", 'start': 3952.949, 'duration': 5.262}, {'end': 3958.811, 'text': 'i can do that.', 'start': 3958.211, 'duration': 0.6}, {'end': 3960.092, 'text': 'i can do that.', 'start': 3959.012, 'duration': 1.08}, {'end': 3960.733, 'text': 'you know.', 'start': 3960.413, 'duration': 0.32}, {'end': 3972.281, 'text': 'what i can do is i can look at i can represent 18 minus 28 as 10 and i can represent 28 minus 50 as 22, and that is this, as i said 1 over n minus 1.', 'start': 3960.733, 'duration': 11.548}, {'end': 3973.322, 'text': 'absolute x.', 'start': 3972.281, 'duration': 1.041}, {'end': 3977.004, 'text': '1. minus x bar plus plus absolute xn.', 'start': 3973.322, 'duration': 3.682}, {'end': 3977.665, 'text': 'minus x bar.', 'start': 3977.004, 'duration': 0.661}, {'end': 3986.711, 'text': "that is this with n minus 1 and this is done as i'm saying this is what is called mean absolute deviation.", 'start': 3977.705, 'duration': 9.006}, {'end': 3994.951, 'text': "and many machine learning algorithms use this you are correct in today's world.", 'start': 3987.83, 'duration': 7.121}, {'end': 3995.991, 'text': 'this is simpler.', 'start': 3995.311, 'duration': 0.68}, {'end': 4002.593, 'text': 'now when standard deviations came up first, this was actually harder.', 'start': 3998.452, 'duration': 4.141}, {'end': 4005.413, 'text': 'but people did argue about this.', 'start': 4004.073, 'duration': 1.34}, {'end': 4010.894, 'text': 'i think well 150 maybe more about i forget my history that much.', 'start': 4007.734, 'duration': 3.16}, {'end': 4015.715, 'text': 'there are two famous mathematicians one named gauss and one named laplace.', 'start': 4011.274, 'duration': 4.441}, {'end': 4028.233, 'text': 'who argued as to whether to use this or whether to use this laplace said you should use this, and gauss said you should use now.', 'start': 4017.227, 'duration': 11.006}, {'end': 4033.497, 'text': 'the reason gauss one was simply because gauss found it easy to do calculations.', 'start': 4028.233, 'duration': 5.264}, {'end': 4042.344, 'text': 'why is this easy to calculate with? because newton had come up with calculus of, you know, a century or so before that.', 'start': 4035.798, 'duration': 6.546}, {'end': 4049.867, 'text': "and so, for example, let's suppose that you want to minimize variability, which is a which is something that we often need to do in analytics,", 'start': 4042.864, 'duration': 7.003}, {'end': 4055.29, 'text': 'which means you need to minimize things with standard deviation, which means you need to differentiate this function.', 'start': 4049.867, 'duration': 5.423}, {'end': 4057.751, 'text': 'the square function is differentiable.', 'start': 4056.13, 'duration': 1.621}, {'end': 4059.632, 'text': 'you can minimize it using calculus.', 'start': 4057.891, 'duration': 1.741}, {'end': 4060.612, 'text': 'this is not.', 'start': 4060.052, 'duration': 0.56}, {'end': 4065.595, 'text': 'so therefore what happened was gauss could do calculations.', 'start': 4062.713, 'duration': 2.882}, {'end': 4069.557, 'text': 'but laplace could not and laplace lost.', 'start': 4066.755, 'duration': 2.802}, {'end': 4073.179, 'text': 'and gauss won the definition of the standard deviation.', 'start': 4071.057, 'duration': 2.122}, {'end': 4080.503, 'text': "we haven't much use 25% of 75 so as in okay.", 'start': 4074.8, 'duration': 5.703}, {'end': 4089.348, 'text': 'okay why do we not do that? so today this entire argument makes no sense.', 'start': 4081.483, 'duration': 7.865}, {'end': 4096.308, 'text': 'because today how do we minimize anything? our computer program.', 'start': 4090.929, 'duration': 5.379}, {'end': 4097.908, 'text': "you don't use any calculus.", 'start': 4096.808, 'duration': 1.1}, {'end': 4103.434, 'text': 'you ask if you run if men or something of that sort you basically run a program to do it.', 'start': 4099.47, 'duration': 3.964}, {'end': 4110.14, 'text': 'so therefore this argument that you can both do calculations equally well with this as in as in that.', 'start': 4104.996, 'duration': 5.144}, {'end': 4115.165, 'text': "so today what is happening is that laplace's way of thinking is being used more and more.", 'start': 4111.582, 'duration': 3.583}, {'end': 4117.787, 'text': 'this one is a lot less sensitive to outliers.', 'start': 4115.946, 'duration': 1.841}, {'end': 4126.014, 'text': 'this one what it does is if it is far away the 22 squares to 484 or something like that, which is a large number.', 'start': 4118.89, 'duration': 7.124}, {'end': 4135.337, 'text': 'so the standard deviation is often driven by very large deviances larger the deviance the more it blows up.', 'start': 4127.394, 'duration': 7.943}, {'end': 4140.438, 'text': 'and so therefore, this is often very criticized.', 'start': 4138.376, 'duration': 2.062}, {'end': 4144.781, 'text': 'if you read, for example, the finance literature, this guy called talib nassim talib,', 'start': 4140.438, 'duration': 4.343}, {'end': 4148.423, 'text': 'or he writes his book called the black swan and fooled by randomness,', 'start': 4144.781, 'duration': 3.642}, {'end': 4151.205, 'text': 'where he left and right criticize the standard deviation as a measure of anything.', 'start': 4148.423, 'duration': 2.782}, {'end': 4160.312, 'text': "so today this argument doesn't make a great deal of sense and when in practice something like this makes sense.", 'start': 4155.008, 'duration': 5.304}], 'summary': 'Explaining mean absolute deviation and standard deviation, discussing historical arguments and practical use cases.', 'duration': 265.905, 'max_score': 3894.407, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY3894407.jpg'}, {'end': 4015.715, 'src': 'embed', 'start': 3987.83, 'weight': 9, 'content': [{'end': 3994.951, 'text': "and many machine learning algorithms use this you are correct in today's world.", 'start': 3987.83, 'duration': 7.121}, {'end': 3995.991, 'text': 'this is simpler.', 'start': 3995.311, 'duration': 0.68}, {'end': 4002.593, 'text': 'now when standard deviations came up first, this was actually harder.', 'start': 3998.452, 'duration': 4.141}, {'end': 4005.413, 'text': 'but people did argue about this.', 'start': 4004.073, 'duration': 1.34}, {'end': 4010.894, 'text': 'i think well 150 maybe more about i forget my history that much.', 'start': 4007.734, 'duration': 3.16}, {'end': 4015.715, 'text': 'there are two famous mathematicians one named gauss and one named laplace.', 'start': 4011.274, 'duration': 4.441}], 'summary': 'Machine learning algorithms use simpler standard deviations. gauss and laplace are famous mathematicians.', 'duration': 27.885, 'max_score': 3987.83, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY3987830.jpg'}, {'end': 4126.014, 'src': 'embed', 'start': 4099.47, 'weight': 0, 'content': [{'end': 4103.434, 'text': 'you ask if you run if men or something of that sort you basically run a program to do it.', 'start': 4099.47, 'duration': 3.964}, {'end': 4110.14, 'text': 'so therefore this argument that you can both do calculations equally well with this as in as in that.', 'start': 4104.996, 'duration': 5.144}, {'end': 4115.165, 'text': "so today what is happening is that laplace's way of thinking is being used more and more.", 'start': 4111.582, 'duration': 3.583}, {'end': 4117.787, 'text': 'this one is a lot less sensitive to outliers.', 'start': 4115.946, 'duration': 1.841}, {'end': 4126.014, 'text': 'this one what it does is if it is far away the 22 squares to 484 or something like that, which is a large number.', 'start': 4118.89, 'duration': 7.124}], 'summary': "Laplace's method is being used more, less sensitive to outliers, 22 squares to 484.", 'duration': 26.544, 'max_score': 4099.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY4099470.jpg'}, {'end': 4226.848, 'src': 'embed', 'start': 4200.148, 'weight': 3, 'content': [{'end': 4207.332, 'text': 'how far how far on the average is an observation from the average confusing statement again.', 'start': 4200.148, 'duration': 7.184}, {'end': 4208.353, 'text': "he's again going to be unhappy.", 'start': 4207.352, 'duration': 1.001}, {'end': 4215.937, 'text': 'but how far on the average is an observation from the average if that answer is 0 that means everything is at the average.', 'start': 4209.613, 'duration': 6.324}, {'end': 4220.467, 'text': "but you're asking the question how far from the average is?", 'start': 4217.366, 'duration': 3.101}, {'end': 4222.387, 'text': 'it is an observation on the average.', 'start': 4220.467, 'duration': 1.92}, {'end': 4226.848, 'text': 'if i take your blood pressure, how far from your average blood pressure is this reading?', 'start': 4222.387, 'duration': 4.461}], 'summary': "The speaker discusses measuring observations' distance from the average, emphasizing the importance of deviations.", 'duration': 26.7, 'max_score': 4200.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY4200148.jpg'}, {'end': 4627.647, 'src': 'embed', 'start': 4583.272, 'weight': 2, 'content': [{'end': 4586.234, 'text': 'the word data means different things to different people.', 'start': 4583.272, 'duration': 2.962}, {'end': 4587.735, 'text': 'to a statistician data means what?', 'start': 4586.234, 'duration': 1.501}, {'end': 4592.497, 'text': 'the statistician data means a number to an IT professional.', 'start': 4589.295, 'duration': 3.202}, {'end': 4596.971, 'text': "what does data mean? bites information, you know, i've lost my data.", 'start': 4592.537, 'duration': 4.434}, {'end': 4600.534, 'text': "i don't particularly care what the data is i've lost my data.", 'start': 4597.592, 'duration': 2.942}, {'end': 4602.755, 'text': 'so this is that information.', 'start': 4601.674, 'duration': 1.081}, {'end': 4604.536, 'text': 'it tells you tells you about the data.', 'start': 4602.775, 'duration': 1.761}, {'end': 4606.718, 'text': "it's an object is a description.", 'start': 4605.237, 'duration': 1.481}, {'end': 4608.919, 'text': "it's a 64-bit stored integer.", 'start': 4606.738, 'duration': 2.181}, {'end': 4610.06, 'text': "it's an object.", 'start': 4609.559, 'duration': 0.501}, {'end': 4611.881, 'text': 'so it tells you about numeric categorical.', 'start': 4610.08, 'duration': 1.801}, {'end': 4615.964, 'text': "it tells you about the kind of data that's available non-null fields.", 'start': 4612.962, 'duration': 3.002}, {'end': 4617.745, 'text': 'in other words, there are objects in the field etc.', 'start': 4616.024, 'duration': 1.721}, {'end': 4627.647, 'text': 'there are so many integer types which are stored at 64 because this computer is probably capable at 64 and there are three categorical variables.', 'start': 4619.864, 'duration': 7.783}], 'summary': 'Data means different things to different people, including numbers, bytes, and information, with variations in interpretation and value.', 'duration': 44.375, 'max_score': 4583.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY4583272.jpg'}], 'start': 3659.931, 'title': 'Statistical measures in data analysis', 'summary': 'Covers standard deviation and mean absolute deviation, emphasizing their significance in measuring data spread and discussing their relevance in modern analytics. it also explores other measures of variability and emphasizes the importance of standardized data format for analytical solutions.', 'chapters': [{'end': 3877.146, 'start': 3659.931, 'title': 'Understanding standard deviation in data analysis', 'summary': 'Explains the concept of standard deviation in data analysis, outlining the formula and its significance in measuring the spread of data, and also touches upon the alternative measure, mean absolute deviation (mad).', 'duration': 217.215, 'highlights': ['Standard deviation is a measure of how spread a typical observation is from the average. It quantifies the extent of variation from the mean, providing a numerical representation of data dispersion.', 'The formula for standard deviation involves calculating the average and then finding the distance of each observation from the average, which is then squared, summed, and averaged. The formula involves multiple steps: calculating the mean, determining the deviation of each data point from the mean, squaring these deviations, summing them up, and finally taking the square root to obtain the standard deviation.', 'Mean absolute deviation (MAD) is an alternative measure of variability, where the absolute value of the deviations is taken without squaring, providing a different perspective on data variability. MAD offers an alternative to standard deviation, focusing on the absolute deviations from the mean without squaring, providing a different measure of variability.']}, {'end': 4192.823, 'start': 3882.328, 'title': 'Understanding mean absolute deviation', 'summary': 'Discusses the concept of mean absolute deviation, its historical background, comparison with standard deviation, and its relevance in modern analytics, highlighting its usage in machine learning and criticism of standard deviation.', 'duration': 310.495, 'highlights': ['The concept of mean absolute deviation is explained using examples of age differences in a dataset, such as the 10-year difference between two individuals and its relevance in understanding data variability.', "Historical debate between mathematicians Laplace and Gauss regarding the use of mean absolute deviation and standard deviation, with Gauss's method prevailing due to its ease of calculation using calculus.", "The criticism of standard deviation by Nassim Taleb in his books 'The Black Swan' and 'Fooled by Randomness', citing its sensitivity to outliers and the resulting large deviations, which makes it unreliable as a measure.", 'The shift towards the usage of mean absolute deviation in modern analytics due to its less sensitivity to outliers and its applicability in machine learning algorithms, reflecting a change in statistical preferences over time.']}, {'end': 4845.086, 'start': 4200.148, 'title': 'Measuring variability in data', 'summary': 'Discusses the concept of variability in data, including measures such as range, interquartile range, and median, and their significance in analyzing and interpreting data, with a focus on understanding the practical implications of these measures in real-life scenarios and their relevance in data curation and visualization.', 'duration': 644.938, 'highlights': ['The concept of variability in data is discussed, including measures such as range, interquartile range, and median. The chapter explains the significance of measures such as range, interquartile range, and median in analyzing and interpreting data, highlighting the practical implications of these measures in real-life scenarios.', 'The relevance of these measures in data curation and visualization is emphasized. The chapter underscores the importance of measures such as range, interquartile range, and median in data curation and visualization, demonstrating their practical relevance in understanding and representing data.', 'The discussion delves into the implications of variability measures in real-life scenarios, such as banking and age demographics. Real-life scenarios, including banking and age demographics, are used to illustrate the practical implications of variability measures, providing insights into their significance in different contexts.']}, {'end': 5336.275, 'start': 4845.106, 'title': 'Archival data format for analytical solutions', 'summary': 'Discusses the importance of storing data in a standardized format for analytical solutions, emphasizing the need for continuity, simplicity, and regulatory compliance in companies, and the trade-offs between efficiency and practicality.', 'duration': 491.169, 'highlights': ['Companies want archival data to be in one format for storage and analysis, such as using Excel and text files, to ensure that algorithms can assume specific data types and run efficiently. Standardizing data format for storage and analysis, using Excel and text files, ensuring algorithms can assume specific data types and run efficiently.', 'Converting continuous data into categories, such as fine classing, allows algorithms to work consistently and avoids the need to rebuild models when new variables are introduced. Converting continuous data into categories, ensuring algorithm consistency, avoiding the need to rebuild models for new variables.', 'Struggle between doing the right thing and the wrong thing in professional analytics, balancing efficiency, time, money, and data, and the cultural differences and regulatory impacts on analytical solutions in companies. Struggle between doing the right thing and the wrong thing in professional analytics, balancing efficiency, time, money, and data, and the cultural differences and regulatory impacts on analytical solutions.', 'The need for continuity in analytical solutions, prioritizing simplicity and obviousness over complexity, to facilitate knowledge transfer and ensure continuity for future analytics teams. The need for continuity in analytical solutions, prioritizing simplicity and obviousness over complexity, to facilitate knowledge transfer and ensure continuity for future analytics teams.', 'Practical challenges in analytical solutions due to imperfect conditions, emphasizing the importance of solving real problems in situations with limited resources and capabilities. Practical challenges in analytical solutions due to imperfect conditions, solving real problems in situations with limited resources and capabilities.']}], 'duration': 1676.344, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY3659931.jpg', 'highlights': ['Standard deviation quantifies data dispersion from the mean.', 'Mean absolute deviation offers an alternative measure of variability.', "Gauss's method for standard deviation prevailed due to ease of calculation.", 'Mean absolute deviation is less sensitive to outliers and applicable in machine learning.', 'Measures like range, interquartile range, and median are significant in data analysis.', 'Standardizing data format ensures algorithms can assume specific data types.', 'Converting continuous data into categories ensures algorithm consistency.', 'Struggle between efficiency, time, money, and data in professional analytics.', 'The need for continuity in analytical solutions prioritizes simplicity and obviousness.', 'Practical challenges in analytical solutions due to imperfect conditions.']}, {'end': 6569.989, 'segs': [{'end': 5419.467, 'src': 'embed', 'start': 5388.733, 'weight': 2, 'content': [{'end': 5390.054, 'text': "this is what's called a box plot.", 'start': 5388.733, 'duration': 1.321}, {'end': 5391.756, 'text': "you've seen a box plot.", 'start': 5390.975, 'duration': 0.781}, {'end': 5393.977, 'text': "there's a box plot.", 'start': 5393.277, 'duration': 0.7}, {'end': 5399.977, 'text': 'people are unsure as to where this box came from.', 'start': 5397.196, 'duration': 2.781}, {'end': 5402.999, 'text': "because there's a sensation called box.", 'start': 5401.658, 'duration': 1.341}, {'end': 5408.221, 'text': "who's used this before but this box came from what it used to be called a box and whisker plot.", 'start': 5404.54, 'duration': 3.681}, {'end': 5408.962, 'text': 'these are the whiskers.', 'start': 5408.301, 'duration': 0.661}, {'end': 5412.043, 'text': 'this whisker will go.', 'start': 5410.823, 'duration': 1.22}, {'end': 5413.844, 'text': 'this is this is the median.', 'start': 5412.563, 'duration': 1.281}, {'end': 5419.467, 'text': 'this is the upper quartile the top edge of the box.', 'start': 5416.425, 'duration': 3.042}], 'summary': 'Explanation of a box plot with key elements such as whiskers and quartiles.', 'duration': 30.734, 'max_score': 5388.733, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY5388733.jpg'}, {'end': 5997.185, 'src': 'embed', 'start': 5967.679, 'weight': 1, 'content': [{'end': 5969.26, 'text': 'should you use one as opposed to the other??', 'start': 5967.679, 'duration': 1.581}, {'end': 5970.961, 'text': 'okay,', 'start': 5970.661, 'duration': 0.3}, {'end': 5974.503, 'text': 'you can use counts as well.', 'start': 5973.362, 'duration': 1.141}, {'end': 5985.676, 'text': 'if you see instead instead of instead of doing it this way instead of seeing it as a table if you want to see it as a plot you can ask for counts.', 'start': 5974.523, 'duration': 11.153}, {'end': 5992.501, 'text': 'so there are things like count plots and bar plots which allow you to do counts in the lab.', 'start': 5986.657, 'duration': 5.844}, {'end': 5994.082, 'text': "you'll do probably a few more of these.", 'start': 5992.541, 'duration': 1.541}, {'end': 5997.185, 'text': 'this is simply another visualization of the same thing.', 'start': 5994.963, 'duration': 2.222}], 'summary': 'You can use count plots and bar plots for visualization of data.', 'duration': 29.506, 'max_score': 5967.679, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY5967679.jpg'}, {'end': 6195.223, 'src': 'embed', 'start': 6160.808, 'weight': 0, 'content': [{'end': 6167.755, 'text': "univariate means i'm looking at it variable by variable one variable at a time when i'm looking at age.", 'start': 6160.808, 'duration': 6.947}, {'end': 6170.518, 'text': "i'm only looking at age.", 'start': 6168.476, 'duration': 2.042}, {'end': 6182.311, 'text': 'so univariate analysis is just a word uni as in uniform same form unicycle cycle with one wheel things like that univariate unit.', 'start': 6171.339, 'duration': 10.972}, {'end': 6194.103, 'text': 'another set of data in replicate the same it will replicate the same nature of the data.', 'start': 6189.401, 'duration': 4.702}, {'end': 6195.223, 'text': "They'll be histogram here again.", 'start': 6194.123, 'duration': 1.1}], 'summary': 'Univariate analysis focuses on one variable at a time, such as age, to examine its distribution.', 'duration': 34.415, 'max_score': 6160.808, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY6160808.jpg'}, {'end': 6569.989, 'src': 'embed', 'start': 6543.762, 'weight': 3, 'content': [{'end': 6555.887, 'text': "so i would ask the question like this that when i see a mirror left and right get switched but top and bottom don't i never understood why you know.", 'start': 6543.762, 'duration': 12.125}, {'end': 6559.026, 'text': 'due to gravity.', 'start': 6558.366, 'duration': 0.66}, {'end': 6562.887, 'text': 'you can think i mean left and right gets with the top and bottom.', 'start': 6559.506, 'duration': 3.381}, {'end': 6567.449, 'text': 'do the mirror and then i thought it was something to do with my eyes, you know, maybe because they left it.', 'start': 6563.087, 'duration': 4.362}, {'end': 6569.989, 'text': "so i looked at it this way and that didn't help.", 'start': 6567.469, 'duration': 2.52}], 'summary': 'Confusion about mirror reflection due to gravity and eyes.', 'duration': 26.227, 'max_score': 6543.762, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY6543762.jpg'}], 'start': 5336.836, 'title': 'Data visualization and analysis', 'summary': 'Covers data distribution in histograms, understanding box plots, and summarizing data for predictive analytics, featuring examples of various visualization tools and statistical tests.', 'chapters': [{'end': 5388.113, 'start': 5336.836, 'title': 'Data analysis and histogram summaries', 'summary': 'Discusses the distribution of variables and skewness in histograms, with most variables having a right skew, and mentions an interesting plot comparison between two tools.', 'duration': 51.277, 'highlights': ['Seaborn has a better version of the interesting plot compared to Matplotlib.', 'Most variables tend to have a right skew in their distribution.', 'Education may have a slight left skew in its distribution, indicating a few highly educated individuals and the majority being less educated.']}, {'end': 5767.978, 'start': 5388.733, 'title': 'Understanding box plots', 'summary': 'Explains the concept of box plots, including the components such as median, quartiles, whiskers, outliers, and their significance in representing data distribution and providing a five-point summary.', 'duration': 379.245, 'highlights': ['Box plots show the median, upper quartile, lower quartile, and whiskers, with the whiskers extending up to 1.5 times the interquartile range above the box. The box plot displays the key statistical measures such as median, upper quartile, lower quartile, and whiskers, with the whiskers extending 1.5 times the interquartile range above the box, providing a visual representation of the spread of the data.', 'Identification of outliers is based on points lying more than 1.5 times the interquartile range above the box, displayed as dots if they are still left outside the whisker. Outliers are identified based on points lying more than 1.5 times the interquartile range above the box, and if any points are still left outside the whisker, they are displayed as dots, providing insights into the presence of extreme values in the data set.', 'The five-point summary in a box plot includes the minimum, lower quartile, median, upper quartile, and maximum, representing the range and distribution of the data. Box plots provide a five-point summary consisting of the minimum, lower quartile, median, upper quartile, and maximum, offering a concise overview of the distribution and range of the dataset.']}, {'end': 6195.223, 'start': 5768.399, 'title': 'Data visualization and analysis', 'summary': 'Explains the significance of summarizing numerical and categorical data for descriptive and predictive analytics, featuring examples of box plots, cross tabulation, chi-square test, counts and pivot tables, and a pair plot analysis.', 'duration': 426.824, 'highlights': ['Significance of Summarizing Data The chapter emphasizes the importance of summarizing numerical and categorical data for descriptive and predictive analytics, showcasing examples of box plots, cross tabulation, chi-square test, counts and pivot tables.', 'Categorical Data Analysis The transcript discusses the use of cross tabulation for analyzing categorical data, providing an example of understanding product usage by different user groups and the potential business problem of identifying user preferences.', 'Predictive Analytics The chapter introduces the concept of using statistical tests, such as the chi-square test, for predictive analytics to measure differences in preferences based on gender or other categorical variables, offering a glimpse into the inference aspect of data analysis.', 'Data Visualization Techniques The transcript explains the use of count plots, bar plots, and pivot tables for visualizing categorical data, highlighting the role of visualization tools in understanding and presenting data effectively.', 'Pair Plot Analysis The chapter concludes with a discussion on pair plot analysis, describing its application in visualizing relationships between variables and its association with univariate analysis for examining individual variables.']}, {'end': 6569.989, 'start': 6199.425, 'title': 'Pair plot command and histograms', 'summary': 'Discusses the pair plot command in python, its handling of object data, and the visualization of histograms, emphasizing the importance of understanding and not manipulating histograms.', 'duration': 370.564, 'highlights': ['The pair plot command in Python ignores object data and plots numerical data, analyzing the relationships between variables. The pair plot command in Python ignores object data and plots numerical data, analyzing the relationships between variables.', 'The histogram is used for visualizing the distribution and count of numerical data, and its shape can be altered by changing the bin width. The histogram is used for visualizing the distribution and count of numerical data, and its shape can be altered by changing the bin width.', "The chapter advises against manipulating histograms unless one has significant experience, emphasizing that changing the bin width can alter the histogram's shape. The chapter advises against manipulating histograms unless one has significant experience, emphasizing that changing the bin width can alter the histogram's shape."]}], 'duration': 1233.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY5336836.jpg', 'highlights': ['Summarizing data is crucial for descriptive and predictive analytics, showcasing examples of box plots, cross tabulation, chi-square test, counts, and pivot tables.', 'Box plots provide a five-point summary consisting of the minimum, lower quartile, median, upper quartile, and maximum, offering a concise overview of the distribution and range of the dataset.', 'The histogram is used for visualizing the distribution and count of numerical data, and its shape can be altered by changing the bin width.', 'Seaborn has a better version of the interesting plot compared to Matplotlib.', 'Most variables tend to have a right skew in their distribution.']}, {'end': 8772.711, 'segs': [{'end': 6682.823, 'src': 'embed', 'start': 6643.823, 'weight': 17, 'content': [{'end': 6646.483, 'text': 'so sometimes data is looked at sort of, you know, numerical.', 'start': 6643.823, 'duration': 2.66}, {'end': 6656.165, 'text': 'and categorical and categorical is sometimes called nominal.', 'start': 6650.484, 'duration': 5.681}, {'end': 6671.379, 'text': "and ordinal nominal means it's a name name of a person north south east and west gender male female place etc.", 'start': 6656.185, 'duration': 15.194}, {'end': 6673.88, 'text': "it's a variable essentially.", 'start': 6671.779, 'duration': 2.101}, {'end': 6677.841, 'text': "it's a name ordinal is it's also categorical but there is a sense of order.", 'start': 6673.9, 'duration': 3.941}, {'end': 6682.823, 'text': 'the reasons of order dissatisfied very dissatisfied.', 'start': 6679.762, 'duration': 3.061}], 'summary': 'Data can be numerical or categorical, with ordinal having a sense of order.', 'duration': 39, 'max_score': 6643.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY6643823.jpg'}, {'end': 6851.445, 'src': 'embed', 'start': 6819.445, 'weight': 4, 'content': [{'end': 6824.168, 'text': 'and so there are some special types of you know problems like zip codes that require special types of solutions.', 'start': 6819.445, 'duration': 4.723}, {'end': 6832.112, 'text': 'so the plot itself is a very very computational plot if it recognizes it as a number a plot set.', 'start': 6825.368, 'duration': 6.744}, {'end': 6837.135, 'text': "if you don't want to make it plot as a number change it to a character.", 'start': 6834.313, 'duration': 2.822}, {'end': 6840.416, 'text': 'most software is including python will allow you to do that.', 'start': 6838.274, 'duration': 2.142}, {'end': 6851.445, 'text': 'now this is in some way a graphical representation of it for the for the end of this session.', 'start': 6845.08, 'duration': 6.365}], 'summary': 'Special solutions for zip codes, computational plot recognition, and graphical representation for the end of the session.', 'duration': 32, 'max_score': 6819.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY6819445.jpg'}, {'end': 7384.933, 'src': 'embed', 'start': 7357.828, 'weight': 2, 'content': [{'end': 7360.809, 'text': "let's say i take five or six stores and i study them extensively.", 'start': 7357.828, 'duration': 2.981}, {'end': 7365.172, 'text': 'how do i know that those results are going to apply to the remainder of my 500 600 stores?', 'start': 7361.31, 'duration': 3.862}, {'end': 7371.816, 'text': 'what is common between these five and the remainder?', 'start': 7368.694, 'duration': 3.122}, {'end': 7373.957, 'text': 'how are they representative of it?', 'start': 7372.576, 'duration': 1.381}, {'end': 7377.339, 'text': 'what part of it applies to the rest and what part of it does not?', 'start': 7374.357, 'duration': 2.982}, {'end': 7379.328, 'text': 'how do i extend it?', 'start': 7378.427, 'duration': 0.901}, {'end': 7382.31, 'text': 'how do i extend your blood pressure readings to the next blood pressure readings?', 'start': 7379.608, 'duration': 2.702}, {'end': 7384.933, 'text': 'how do i figure this out?', 'start': 7384.012, 'duration': 0.921}], 'summary': 'Addressing the challenge of extending findings from a small sample to a larger population of 500-600 stores and understanding the commonalities and representative aspects between the two sets.', 'duration': 27.105, 'max_score': 7357.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY7357828.jpg'}, {'end': 7666.205, 'src': 'embed', 'start': 7638.888, 'weight': 1, 'content': [{'end': 7642.511, 'text': 'after it flows out of the living order, understand whether the liver is filtering your blood correctly or not.', 'start': 7638.888, 'duration': 3.623}, {'end': 7644.937, 'text': 'now to do that.', 'start': 7644.377, 'duration': 0.56}, {'end': 7647.658, 'text': 'you need to draw the blood in very specific places.', 'start': 7644.977, 'duration': 2.681}, {'end': 7654.361, 'text': 'so in order to do that therefore you your experimentation should cover all of that.', 'start': 7650.279, 'duration': 4.082}, {'end': 7660.423, 'text': "what does that mean? for example in business terms? let's say that you're looking at sales data and you want to understand your sales distribution.", 'start': 7654.701, 'duration': 5.722}, {'end': 7662.944, 'text': "well, don't focus on certain salespeople.", 'start': 7660.843, 'duration': 2.101}, {'end': 7664.324, 'text': 'look at your bad salespeople.', 'start': 7663.204, 'duration': 1.12}, {'end': 7666.205, 'text': 'look at your good salespeople.', 'start': 7664.344, 'duration': 1.861}], 'summary': 'To assess liver function, draw blood from specific places; similarly, in business, analyze both bad and good salespeople.', 'duration': 27.317, 'max_score': 7638.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY7638888.jpg'}, {'end': 7958.327, 'src': 'embed', 'start': 7926.332, 'weight': 15, 'content': [{'end': 7930.293, 'text': 'ah k 53 3 6 1 9 with his driver has not been seen by your data set.', 'start': 7926.332, 'duration': 3.961}, {'end': 7938.336, 'text': "how are you crossing? because you're making the assumption that while i have not seen him i've seen many others like him.", 'start': 7931.254, 'duration': 7.082}, {'end': 7951.381, 'text': "so so there's this story, right? so, you know, a taxi driver is going at night on the road, etc.", 'start': 7942.798, 'duration': 8.583}, {'end': 7954.482, 'text': "etc and he's just running left right left right.", 'start': 7951.401, 'duration': 3.081}, {'end': 7958.327, 'text': 'so red light cross no issues etc.', 'start': 7954.502, 'duration': 3.825}], 'summary': 'Taxi driver behavior observed at night, crossing red lights without issues.', 'duration': 31.995, 'max_score': 7926.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY7926332.jpg'}, {'end': 8320.901, 'src': 'embed', 'start': 8287.995, 'weight': 16, 'content': [{'end': 8289.575, 'text': "it's harder you need more data.", 'start': 8287.995, 'duration': 1.58}, {'end': 8300.546, 'text': 'not necessarily the variation of the average would go less.', 'start': 8297.222, 'duration': 3.324}, {'end': 8305.031, 'text': "so let's suppose that you have no control over your diet.", 'start': 8302.188, 'duration': 2.843}, {'end': 8306.633, 'text': "i'm not accusing you of anything.", 'start': 8305.572, 'duration': 1.061}, {'end': 8315.123, 'text': "it happens to humans, but let's suppose that you are doing a job in which your lifestyle is very varied.", 'start': 8309.056, 'duration': 6.067}, {'end': 8319.259, 'text': 'you travel from place to place you eat in different hotels.', 'start': 8316.257, 'duration': 3.002}, {'end': 8320.901, 'text': "sometimes you don't eat at all.", 'start': 8319.68, 'duration': 1.221}], 'summary': 'Variation in diet can make it harder to maintain average data, especially with a varied lifestyle.', 'duration': 32.906, 'max_score': 8287.995, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY8287995.jpg'}, {'end': 8387.074, 'src': 'embed', 'start': 8363.922, 'weight': 12, 'content': [{'end': 8372.369, 'text': "take a glucometer and before going to bed, do this, or after you've just had a very hard day, do this.", 'start': 8363.922, 'duration': 8.447}, {'end': 8376.05, 'text': 'i can give certain instructions to cover all the corners.', 'start': 8373.809, 'duration': 2.241}, {'end': 8378.911, 'text': "or i can simply say i don't know.", 'start': 8377.17, 'duration': 1.741}, {'end': 8387.074, 'text': 'but what you need to do is you need to measure your blood pressure or, sorry, your blood sugar, say every six hours and then tell me what happens.', 'start': 8378.911, 'duration': 8.163}], 'summary': 'Measure blood sugar every 6 hours and report findings.', 'duration': 23.152, 'max_score': 8363.922, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY8363922.jpg'}, {'end': 8433.966, 'src': 'embed', 'start': 8408.069, 'weight': 3, 'content': [{'end': 8415.634, 'text': "what will you what will you do? i mean, how will you measure it? you've just introduced it? based on past data, you'll do that.", 'start': 8408.069, 'duration': 7.565}, {'end': 8416.434, 'text': "but you've just released it.", 'start': 8415.654, 'duration': 0.78}, {'end': 8417.615, 'text': 'you can measure current data.', 'start': 8416.454, 'duration': 1.161}, {'end': 8422.939, 'text': 'no different situation.', 'start': 8420.797, 'duration': 2.142}, {'end': 8426.141, 'text': "i've just released the product all that is over all that is over.", 'start': 8422.999, 'duration': 3.142}, {'end': 8429.963, 'text': 'i now have just released this this watch in the market.', 'start': 8426.241, 'duration': 3.722}, {'end': 8433.966, 'text': 'what typically happens is people track the market very very closely.', 'start': 8431.284, 'duration': 2.682}], 'summary': 'Measuring product success based on past and current data after release in the market.', 'duration': 25.897, 'max_score': 8408.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY8408069.jpg'}, {'end': 8725.853, 'src': 'embed', 'start': 8698.263, 'weight': 0, 'content': [{'end': 8702.145, 'text': "there have also been times in my life when i've been a lot more conscious of what others think about me.", 'start': 8698.263, 'duration': 3.882}, {'end': 8703.826, 'text': 'you can imagine what points in my life.', 'start': 8702.485, 'duration': 1.341}, {'end': 8706.567, 'text': "so now i groom i'm very careful.", 'start': 8705.026, 'duration': 1.541}, {'end': 8708.888, 'text': "i get my hair done and you know i'm all corrected.", 'start': 8706.607, 'duration': 2.281}, {'end': 8711.049, 'text': "i'm getting my haircut much more regularly.", 'start': 8708.908, 'duration': 2.141}, {'end': 8719.814, 'text': "now what am i doing? so in the second case what i'm doing is i'm trying to make sure that i'm reaching a certain distributional standard.", 'start': 8713.611, 'duration': 6.203}, {'end': 8725.853, 'text': "it was a certain target distribution that i have and i'm interested in getting there and intolerant of variability.", 'start': 8721.189, 'duration': 4.664}], 'summary': 'Striving for a certain distributional standard, grooming more regularly to meet target distribution.', 'duration': 27.59, 'max_score': 8698.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY8698263.jpg'}], 'start': 6571.93, 'title': 'Data distributions and statistical analysis', 'summary': 'Covers the significance of understanding categorical and ordinal variables, data distribution, statistical inference, and decision-making. it emphasizes the importance of abstracting away from random variations, visual representation of distributions, and the relationship between distribution and density functions in statistics.', 'chapters': [{'end': 6840.416, 'start': 6571.93, 'title': 'Understanding categorical and ordinal variables', 'summary': 'Explains the significance of ordinal and categorical variables using fitness ratings as an example, highlighting the challenges of treating them as numbers and the complexities regarding zip codes as categorical variables.', 'duration': 268.486, 'highlights': ['The fitness variable has a range of one to five, with one representing poor fitness and five indicating excellent fitness. Clarifies the range and meaning of the fitness variable, providing context for the ordinal nature of the variable.', 'The challenges of treating ordinal variables as numerical and understanding the differences between nominal and ordinal variables are highlighted. Emphasizes the difficulties in interpreting ordinal variables and distinguishing between nominal and ordinal data.', 'The complexities associated with zip codes as categorical variables are discussed, including the growth of categories with the data set and the inability to perform arithmetic operations on zip codes. Addresses the unique challenges of zip codes as categorical variables and the issues arising from the expanding number of categories.']}, {'end': 7197.575, 'start': 6845.08, 'title': 'Understanding data distribution and statistical analysis', 'summary': 'Discusses the calculation of mean and standard deviation for data, the concept of data distribution, and the importance of understanding population distribution for predicting future customer behavior based on past data.', 'duration': 352.495, 'highlights': ['The mean of the data is 27.7888, and the standard deviation can be calculated using the formula provided, enabling statistical analysis (e.g., trimming) (Quantifiable: mean value)', 'The distribution plot in Seaborn aims to analyze the underlying distribution of the data, providing insights into the population distribution and the challenge of predicting future customer behavior based on past customer data (Quantifiable: sample size of 180 observations)', 'The process involves assuming a population distribution from which samples are taken, highlighting the importance of understanding the commonality between observed and future customer data for making predictions and business decisions (Quantifiable: sample size, future customer behavior)', 'The graphical representation calculates the distribution by taking averages of points, contributing to the understanding of population distribution and the analysis of new customer data (Quantifiable: sample calculations)']}, {'end': 7379.328, 'start': 7198.416, 'title': 'Understanding data distributions', 'summary': 'Discusses the importance of understanding data distributions in making predictions, emphasizing the need to abstract away from random variations and identify systematic patterns from the data to make future projections.', 'duration': 180.912, 'highlights': ['The importance of understanding data distributions in making predictions is emphasized, highlighting the need to abstract away from random variations and identify systematic patterns from the data to make future projections. Emphasizes the need to understand data distributions for making predictions, abstracting away from random variations and identifying systematic patterns.', 'The example of blood pressure readings is used to illustrate the concept, emphasizing the ability to make projections about future readings based on the current data. Illustrates the concept of making projections based on current data, using blood pressure readings as an example.', 'The discussion extends to the application of understanding data distributions in analyzing business performance across multiple stores, highlighting the need to identify what is common and representative among different sets of data. Applies the concept to business performance analysis across multiple stores, emphasizing the need to identify common and representative factors.']}, {'end': 7654.361, 'start': 7379.608, 'title': 'Statistical inference and distribution plots', 'summary': 'Discusses statistical inference and distribution plots, emphasizing the importance of sampling variability and the visual representation of underlying distributions.', 'duration': 274.753, 'highlights': ['The chapter explains statistical inference and the estimation of underlying true distribution using distribution plots. It discusses the concept of statistical inference and the estimation of underlying true distribution using distribution plots, emphasizing the importance of these techniques in understanding data variability.', 'Sampling variability is emphasized, indicating the impact of taking a sample on the understanding of the underlying truth. It highlights the impact of sampling variability on understanding the underlying truth, emphasizing the limitations of taking a sample in representing the true nature of data.', 'The importance of specific sampling situations, such as before and after eating, and for certain diseases, is discussed in relation to blood sample analysis. It emphasizes the importance of specific sampling situations, such as before and after eating, and for certain diseases, in analyzing blood samples to gain a comprehensive understanding of blood characteristics.']}, {'end': 7839.982, 'start': 7654.701, 'title': 'Understanding data distribution in business', 'summary': 'Emphasizes the importance of covering the range of possibilities in analyzing sales data to understand distribution, and explains the relationship between distribution function and density function in statistics.', 'duration': 185.281, 'highlights': ['The importance of covering the range of possibilities in analyzing sales data to understand distribution By looking at both bad and good salespeople, as well as high and low selling products, one can gain insights into sales distribution.', 'Explanation of the relationship between distribution function and density function in statistics The distribution function is the integral of the density function, and the density function is the derivative of the distribution function, providing a framework for drawing conclusions outside the given data.', 'Challenges in drawing conclusions from a single sample The difficulty of deriving a distribution that applies to everyone based on only one sample is highlighted as a major problem in statistics.']}, {'end': 8223.848, 'start': 7841.143, 'title': 'Decision-making and risk strategy', 'summary': 'Discusses the importance of analyzing risks and making strategic decisions based on past data and distributions, emphasizing the need to translate logical thinking into algorithms for companies and computers.', 'duration': 382.705, 'highlights': ['The importance of analyzing risks and making strategic decisions based on past data and distribution is emphasized. The bank, clothing store, and great learning are advised to analyze their portfolios, sales, and course reviews, respectively, to determine their risk strategies and decision-making processes.', 'The need to translate logical thinking into algorithms for companies and computers is emphasized. The objective of an analytics professional is to translate logical thinking into algorithms and procedures that the company and the computer can understand, which is a challenging task.', "The process of determining salary negotiation based on expenditures and expected income is discussed. The process involves understanding one's expenditures, expected income, and how these factors are based on past data and distributions.", 'The significance of using past data and distributions to make decisions is illustrated through the analogy of crossing the road based on past experiences. The decision-making process is likened to standing on a road and deciding whether to cross it, using past experiences and data to make logical assumptions.', 'The concept of translating logical thinking into an algorithm is described in the context of determining the average age of customers. The challenge of translating logical thinking into an algorithm is illustrated through the example of estimating the average age of customers and understanding the relationship between new data and past data.']}, {'end': 8772.711, 'start': 8223.848, 'title': 'Descriptive analytics and data variability', 'summary': 'Discusses the importance of descriptive analytics, data variability, and the need for more data when facing unknown distributions, emphasizing the significance of understanding and utilizing data efficiently in addressing different problems.', 'duration': 548.863, 'highlights': ['The importance of descriptive analytics and the need for more data when facing unknown distributions The chapter emphasizes the significance of descriptive analytics and the need for more data when dealing with unknown distributions, highlighting the importance of understanding the variability of data and utilizing it efficiently to address different problems.', 'The significance of understanding and utilizing data efficiently in addressing different problems The discussion emphasizes the importance of understanding and efficiently utilizing data in addressing various problems, underscoring the need to adapt to data variability and utilize it effectively to solve different challenges.', 'The impact of data variability in measuring blood sugar and introducing new products The chapter illustrates the impact of data variability in scenarios such as measuring blood sugar in individuals with highly variable lifestyles and introducing new products in the market, emphasizing the need to adapt measurement strategies and track market changes in response to unknown distributions.']}], 'duration': 2200.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY6571930.jpg', 'highlights': ['The fitness variable has a range of one to five, with one representing poor fitness and five indicating excellent fitness.', 'The challenges of treating ordinal variables as numerical and understanding the differences between nominal and ordinal variables are highlighted.', 'The complexities associated with zip codes as categorical variables are discussed, including the growth of categories with the data set and the inability to perform arithmetic operations on zip codes.', 'The mean of the data is 27.7888, and the standard deviation can be calculated using the formula provided, enabling statistical analysis (e.g., trimming)', 'The distribution plot in Seaborn aims to analyze the underlying distribution of the data, providing insights into the population distribution and the challenge of predicting future customer behavior based on past customer data (Sample size: 180 observations)', 'The process involves assuming a population distribution from which samples are taken, highlighting the importance of understanding the commonality between observed and future customer data for making predictions and business decisions (Sample size, future customer behavior)', 'The graphical representation calculates the distribution by taking averages of points, contributing to the understanding of population distribution and the analysis of new customer data (Sample calculations)', 'The importance of understanding data distributions in making predictions is emphasized, highlighting the need to abstract away from random variations and identify systematic patterns from the data to make future projections.', 'The example of blood pressure readings is used to illustrate the concept, emphasizing the ability to make projections about future readings based on the current data.', 'The discussion extends to the application of understanding data distributions in analyzing business performance across multiple stores, highlighting the need to identify what is common and representative among different sets of data.', 'The chapter explains statistical inference and the estimation of underlying true distribution using distribution plots, emphasizing the importance of these techniques in understanding data variability.', 'Sampling variability is emphasized, indicating the impact of taking a sample on the understanding of the underlying truth.', 'The importance of specific sampling situations, such as before and after eating, and for certain diseases, is discussed in relation to blood sample analysis.', 'The importance of covering the range of possibilities in analyzing sales data to understand distribution By looking at both bad and good salespeople, as well as high and low selling products, one can gain insights into sales distribution.', 'Explanation of the relationship between distribution function and density function in statistics The distribution function is the integral of the density function, and the density function is the derivative of the distribution function, providing a framework for drawing conclusions outside the given data.', 'The importance of analyzing risks and making strategic decisions based on past data and distribution is emphasized.', 'The need to translate logical thinking into algorithms for companies and computers is emphasized.', 'The process of determining salary negotiation based on expenditures and expected income is discussed.', 'The significance of using past data and distributions to make decisions is illustrated through the analogy of crossing the road based on past experiences.', 'The concept of translating logical thinking into an algorithm is described in the context of determining the average age of customers.', 'The importance of descriptive analytics and the need for more data when facing unknown distributions The chapter emphasizes the significance of descriptive analytics and the need for more data when dealing with unknown distributions, highlighting the importance of understanding the variability of data and utilizing it efficiently to address different problems.', 'The significance of understanding and utilizing data efficiently in addressing different problems The discussion emphasizes the importance of understanding and efficiently utilizing data in addressing various problems, underscoring the need to adapt to data variability and utilize it effectively to solve different challenges.', 'The impact of data variability in measuring blood sugar and introducing new products The chapter illustrates the impact of data variability in scenarios such as measuring blood sugar in individuals with highly variable lifestyles and introducing new products in the market, emphasizing the need to adapt measurement strategies and track market changes in response to unknown distributions.']}, {'end': 11556.678, 'segs': [{'end': 9429.453, 'src': 'embed', 'start': 9397.755, 'weight': 5, 'content': [{'end': 9399.416, 'text': 'so how would you come up with that number??', 'start': 9397.755, 'duration': 1.661}, {'end': 9400.197, 'text': "what's, what's, what's?", 'start': 9399.616, 'duration': 0.581}, {'end': 9401.338, 'text': 'a fair answer to that??', 'start': 9400.197, 'duration': 1.141}, {'end': 9408.074, 'text': 'a mean see if i do the mean here is how i would do it on a given day.', 'start': 9404.17, 'duration': 3.904}, {'end': 9409.074, 'text': 'i would.', 'start': 9408.094, 'duration': 0.98}, {'end': 9410.836, 'text': "so the first website i've gone to.", 'start': 9409.074, 'duration': 1.762}, {'end': 9412.858, 'text': "i'll find out how much time i spend there.", 'start': 9410.836, 'duration': 2.022}, {'end': 9414.159, 'text': 'second, how much time i spent.', 'start': 9412.858, 'duration': 1.301}, {'end': 9415.7, 'text': 'the third, how much time i spend.', 'start': 9414.159, 'duration': 1.541}, {'end': 9417.302, 'text': 'the fourth, how much time i spent that?', 'start': 9415.7, 'duration': 1.602}, {'end': 9419.624, 'text': 'and then add this up and i divide.', 'start': 9417.302, 'duration': 2.322}, {'end': 9421.265, 'text': "that's the mean, right?", 'start': 9419.624, 'duration': 1.641}, {'end': 9422.346, 'text': 'what would be the median?', 'start': 9421.586, 'duration': 0.76}, {'end': 9425.589, 'text': 'the median would be.', 'start': 9424.909, 'duration': 0.68}, {'end': 9429.453, 'text': "i'd look at all those times and i sought it and i put this in, which is going to be larger.", 'start': 9425.589, 'duration': 3.864}], 'summary': 'Determining average and median time spent on websites for analysis.', 'duration': 31.698, 'max_score': 9397.755, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY9397755.jpg'}, {'end': 9687.838, 'src': 'embed', 'start': 9655.065, 'weight': 4, 'content': [{'end': 9657.725, 'text': "it's somebody somewhere around 55,000 where this maximum is.", 'start': 9655.065, 'duration': 2.66}, {'end': 9663.026, 'text': 'correct for women.', 'start': 9661.506, 'duration': 1.52}, {'end': 9663.606, 'text': "it's here.", 'start': 9663.086, 'duration': 0.52}, {'end': 9668.543, 'text': 'maybe just less than 50,000.', 'start': 9664.087, 'duration': 4.456}, {'end': 9674.468, 'text': 'so you understand what the mode is it is the it is the highest frequency or the most common value.', 'start': 9668.544, 'duration': 5.924}, {'end': 9679.292, 'text': "but in practice that's actually a little difficult to do.", 'start': 9676.75, 'duration': 2.542}, {'end': 9682.334, 'text': 'if i give you a set of numbers, how will you calculate the mode?', 'start': 9679.292, 'duration': 3.042}, {'end': 9687.158, 'text': 'but will you see a spike?', 'start': 9686.257, 'duration': 0.901}, {'end': 9687.838, 'text': 'what is the spike??', 'start': 9687.218, 'duration': 0.62}], 'summary': 'The maximum income is around 55,000 for women, and understanding the mode as the highest frequency value in a set of numbers.', 'duration': 32.773, 'max_score': 9655.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY9655065.jpg'}, {'end': 10691.506, 'src': 'embed', 'start': 10642.531, 'weight': 1, 'content': [{'end': 10647.172, 'text': "so we had we have to build a story around this when we went to the cmd and said that you know, here's what we had done.", 'start': 10642.531, 'duration': 4.641}, {'end': 10652.374, 'text': 'so you can make our story around this and the story we made up correctly or incorrectly.', 'start': 10648.413, 'duration': 3.961}, {'end': 10664.215, 'text': "i don't know is that in the beginning to some extent people have low dependencies typically coming your unmarried bachelor etcetera.", 'start': 10654.454, 'duration': 9.761}, {'end': 10666.697, 'text': "you're also ready to work a lot harder.", 'start': 10664.916, 'duration': 1.781}, {'end': 10669.139, 'text': 'so staying close by is convenient.', 'start': 10666.937, 'duration': 2.202}, {'end': 10675.745, 'text': 'you get a pg or you get an apartment, you stay close to you, stay close to work,', 'start': 10671.121, 'duration': 4.624}, {'end': 10680.368, 'text': 'because staying further away from work gets gets you no particular benefit is just inconvenient.', 'start': 10675.745, 'duration': 4.623}, {'end': 10685.693, 'text': 'but as you as you reach in some way middle age, so to speak things become very complicated.', 'start': 10681.589, 'duration': 4.104}, {'end': 10688.065, 'text': 'there is a spouse here.', 'start': 10687.125, 'duration': 0.94}, {'end': 10689.265, 'text': 'he she may have a job.', 'start': 10688.125, 'duration': 1.14}, {'end': 10691.506, 'text': 'there are kids there are schools.', 'start': 10689.726, 'duration': 1.78}], 'summary': 'Workers prioritize proximity to work; family adds complexity.', 'duration': 48.975, 'max_score': 10642.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY10642531.jpg'}, {'end': 11432.829, 'src': 'embed', 'start': 11386.897, 'weight': 0, 'content': [{'end': 11388.537, 'text': 'yes, if you want to give it a fancy name correct.', 'start': 11386.897, 'duration': 1.64}, {'end': 11389.758, 'text': "well, you're right.", 'start': 11389.398, 'duration': 0.36}, {'end': 11390.118, 'text': 'right right.', 'start': 11389.798, 'duration': 0.32}, {'end': 11391.799, 'text': 'that is a parabola undoubtedly true.', 'start': 11390.138, 'duration': 1.661}, {'end': 11401.092, 'text': "but you could argue as to why is it height by weight squared? that's a slightly different question.", 'start': 11394.087, 'duration': 7.005}, {'end': 11411.98, 'text': "why isn't it say weight by height? so let's suppose that you so weight by height means what so let's suppose that these two they're not the same.", 'start': 11401.372, 'duration': 10.608}, {'end': 11417.765, 'text': "well, let's suppose that they're so these two so this is a certain height.", 'start': 11413.602, 'duration': 4.163}, {'end': 11418.565, 'text': 'this is a certain height.', 'start': 11417.785, 'duration': 0.78}, {'end': 11421.668, 'text': 'if i put this on top of this, what happens to the weight?', 'start': 11418.966, 'duration': 2.702}, {'end': 11425.704, 'text': 'if these two are exactly the same, this is going to double.', 'start': 11423.243, 'duration': 2.461}, {'end': 11427.486, 'text': "or if i take two of these, i don't see two of them.", 'start': 11425.704, 'duration': 1.782}, {'end': 11428.006, 'text': 'i apologize.', 'start': 11427.546, 'duration': 0.46}, {'end': 11428.526, 'text': 'but anyway, okay.', 'start': 11428.026, 'duration': 0.5}, {'end': 11429.467, 'text': "so here's one more.", 'start': 11428.826, 'duration': 0.641}, {'end': 11432.829, 'text': 'so these two so if i do if i put this on top of this is doubles.', 'start': 11429.787, 'duration': 3.042}], 'summary': 'Discussion on parabola and weight-height relationship.', 'duration': 45.932, 'max_score': 11386.897, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY11386897.jpg'}], 'start': 8772.711, 'title': 'Data distributions and statistical analysis', 'summary': 'Delves into the importance of assumptions in statistical analysis, challenges of industry-specific data distributions, understanding mean, median, mode, and correlation, emphasizing the relevance and implications of statistical methods in various contexts.', 'chapters': [{'end': 8938.757, 'start': 8772.711, 'title': 'Advertising cardio product', 'summary': 'Discusses the importance of making assumptions about data distributions in statistics to simplify calculations, emphasizing that while assumptions ease calculations, they do not guarantee accuracy, and models may still be useful even if assumptions are incorrect.', 'duration': 166.046, 'highlights': ["Statisticians make assumptions about data distributions for easier calculations, but an assumption doesn't guarantee accuracy. Statisticians assume data follows a normal distribution to simplify calculations, but this assumption doesn't ensure accuracy.", "Models may still be useful even if the assumptions about data distributions are incorrect. The statement 'all models are wrong, but some models are useful' emphasizes the potential usefulness of models even when assumptions about data distributions are incorrect.", 'In engineering, data may have a specific shape, like a Weibull distribution, which needs to be considered for accurate reporting. In engineering, data may follow a Weibull distribution, indicating the importance of considering specific data shapes for accurate reporting.']}, {'end': 9313.638, 'start': 8939.318, 'title': 'Industry distributions and statistical complexities', 'summary': 'Discusses the challenges of industry-specific data distributions, the implications of making assumptions in statistical analysis, and the concept of pack learning in machine learning, emphasizing the importance of generalizability in statistical methods.', 'duration': 374.32, 'highlights': ['The concept of pack learning in machine learning, which emphasizes a probabilistic and approximate approach, is explained, highlighting the importance of generalizability in statistical methods.', 'The challenges of making assumptions in statistical analysis are discussed, illustrating the risks and complexities involved in deviating from standard distributions and methodologies.', 'The implications of industry-specific data distributions and the resistance to change due to historical success and regulatory constraints are highlighted, emphasizing the importance of adhering to standard practices in certain industries.', "The comparison between using mean and median in estimating population parameters, based on the distribution's symmetry and the presence of outliers, is explained to guide statistical decision-making.", 'The analogy of an accountant following standard practices and the implications of deviating from them are used to illustrate the challenges of using non-standard statistical methods in certain contexts.']}, {'end': 9790.15, 'start': 9315.259, 'title': 'Understanding mean, median, and mode', 'summary': 'Explores the concepts of mean, median, and mode, and their implications in understanding data distribution and user behavior when browsing websites, highlighting the relevance of these measures in different contexts and the challenges in calculating the mode.', 'duration': 474.891, 'highlights': ['The difference between mean and median signifies the browsing behavior, indicating if users are spending more time on specific websites or casually browsing across various sites. Implication of mean and median in understanding user behavior, relevance of these measures in different contexts, browsing habits', 'The mode, representing the most common value in a distribution, is less discussed due to its complexity in calculation, especially with numerical data. Complexity in calculating the mode, its relevance in data analysis with categorical data', 'Challenges in calculating the mode are highlighted, emphasizing its decreased popularity in data analysis due to the difficulty in obtaining it algorithmically. Difficulty in obtaining mode algorithmically, decreased popularity in data analysis']}, {'end': 10714.071, 'start': 9790.15, 'title': 'Statistics and distributions', 'summary': 'Covers the comparison of histograms, the analysis of bivariate distributions, and the measurement of correlation through covariance, emphasizing the relevance of statistics in understanding relationships between variables.', 'duration': 923.921, 'highlights': ['The chapter discusses the comparison of histograms by different variables to understand the difference in distributions, providing insights into gender and other variables. Comparison of histograms by different variables, insights into gender and other variables', 'The analysis of bivariate distributions is introduced, focusing on the measurement of correlation and its relevance in understanding the relationship between two variables. Introduction of bivariate distributions, measurement of correlation, understanding the relationship between two variables', 'The concept of covariance is explained as a measure of the nature of the relationship between two variables, highlighting the implications of positive, negative, or zero covariance on the direction and strength of the relationship. Explanation of covariance, implications of positive, negative, or zero covariance on the relationship']}, {'end': 11556.678, 'start': 10714.111, 'title': 'Understanding correlation in statistics', 'summary': 'Explains the concept of correlation in statistics, highlighting the range of correlation values, the significance of normalization to eliminate unit dependency, and the interpretation of correlation values as measures of linear relationships between variables.', 'duration': 842.567, 'highlights': ['Correlation values range from -1 to 1, indicating the strength and direction of the relationship between variables. The concept of correlation is explained in terms of its range, providing a clear understanding of the strength and direction of the relationship between variables.', 'Normalization is used to eliminate unit dependency by dividing the values by the standard deviations of the respective variables. The significance of normalization in eliminating unit dependency is highlighted, emphasizing its role in standardizing the values for meaningful correlation interpretation.', 'Correlation values represent measures of linear relationships between variables, with a close correlation to +1 indicating a strong positive relationship. The interpretation of correlation values as measures of linear relationships, particularly a strong positive relationship when the correlation is close to +1, is emphasized, providing insights into the nature of the relationship between variables.']}], 'duration': 2783.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY8772711.jpg', 'highlights': ['In engineering, data may follow a Weibull distribution, indicating the importance of considering specific data shapes for accurate reporting.', 'The challenges of making assumptions in statistical analysis are discussed, illustrating the risks and complexities involved in deviating from standard distributions and methodologies.', 'The implications of industry-specific data distributions and the resistance to change due to historical success and regulatory constraints are highlighted, emphasizing the importance of adhering to standard practices in certain industries.', "The comparison between using mean and median in estimating population parameters, based on the distribution's symmetry and the presence of outliers, is explained to guide statistical decision-making.", 'The analogy of an accountant following standard practices and the implications of deviating from them are used to illustrate the challenges of using non-standard statistical methods in certain contexts.', 'The concept of pack learning in machine learning, which emphasizes a probabilistic and approximate approach, is explained, highlighting the importance of generalizability in statistical methods.', 'The difference between mean and median signifies the browsing behavior, indicating if users are spending more time on specific websites or casually browsing across various sites.', 'The concept of covariance is explained as a measure of the nature of the relationship between two variables, highlighting the implications of positive, negative, or zero covariance on the direction and strength of the relationship.', 'Correlation values range from -1 to 1, indicating the strength and direction of the relationship between variables.', 'Normalization is used to eliminate unit dependency by dividing the values by the standard deviations of the respective variables.', 'Correlation values represent measures of linear relationships between variables, with a close correlation to +1 indicating a strong positive relationship.']}, {'end': 14462.667, 'segs': [{'end': 11584.224, 'src': 'embed', 'start': 11557.799, 'weight': 6, 'content': [{'end': 11564.002, 'text': 'so. therefore, this relationship depends on the empirical relationship between height and weight, for the data that is available,', 'start': 11557.799, 'duration': 6.203}, {'end': 11565.062, 'text': 'which is of humans growing.', 'start': 11564.002, 'duration': 1.06}, {'end': 11570.485, 'text': 'and so empirically people have discovered that this is the object that should be invariant.', 'start': 11566.603, 'duration': 3.882}, {'end': 11574.895, 'text': "this is an example of what's called dimension reduction.", 'start': 11572.793, 'duration': 2.102}, {'end': 11584.224, 'text': 'two variables are being combined into one, which is carrying information for you, but it relies on a nonlinear relationship between the two.', 'start': 11576.537, 'duration': 7.687}], 'summary': "Relationship between height and weight used for dimension reduction in humans' growth data.", 'duration': 26.425, 'max_score': 11557.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY11557799.jpg'}, {'end': 11883.962, 'src': 'embed', 'start': 11841.792, 'weight': 3, 'content': [{'end': 11855.582, 'text': "is there a descriptive way of getting at that equation? so what this does this kind of an equation is what's called a linear regression model.", 'start': 11841.792, 'duration': 13.79}, {'end': 11858.264, 'text': 'this is your first model.', 'start': 11856.883, 'duration': 1.381}, {'end': 11865.596, 'text': 'this is going from descriptive to predictive.', 'start': 11861.055, 'duration': 4.541}, {'end': 11870.978, 'text': "i haven't done it yet.", 'start': 11870.298, 'duration': 0.68}, {'end': 11873.018, 'text': "i haven't done it yet.", 'start': 11870.998, 'duration': 2.02}, {'end': 11874.139, 'text': "i'm just saying what i'm trying to do.", 'start': 11873.119, 'duration': 1.02}, {'end': 11878, 'text': 'self rated fitness on a one to five scale.', 'start': 11875.959, 'duration': 2.041}, {'end': 11883.962, 'text': "i'll get there.", 'start': 11883.522, 'duration': 0.44}], 'summary': 'Introduction to linear regression model for predicting fitness on a 1-5 scale.', 'duration': 42.17, 'max_score': 11841.792, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY11841792.jpg'}, {'end': 12064.594, 'src': 'embed', 'start': 12032.697, 'weight': 2, 'content': [{'end': 12035.58, 'text': 'it will simply describe the nature of the relationship to you.', 'start': 12032.697, 'duration': 2.883}, {'end': 12038.923, 'text': 'it will make no causal inference.', 'start': 12036.501, 'duration': 2.422}, {'end': 12043.347, 'text': 'no sense that this causes this it will give you no predictive model.', 'start': 12039.503, 'duration': 3.844}, {'end': 12052.215, 'text': 'it simply describes and will discuss how it describes to it predicts predicts means when i put in another value of x.', 'start': 12043.948, 'duration': 8.267}, {'end': 12056.532, 'text': 'and another value of x1 and another value of x2.', 'start': 12054.571, 'duration': 1.961}, {'end': 12064.594, 'text': "i will get a different value of y, which means that i've looked at data from all of you, and a new person comes into the room with a new x1 and x2,", 'start': 12056.692, 'duration': 7.902}], 'summary': 'Describing relationship with no causal inference or predictive model, only predicts based on different x values.', 'duration': 31.897, 'max_score': 12032.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY12032697.jpg'}, {'end': 13274.377, 'src': 'embed', 'start': 13243.513, 'weight': 5, 'content': [{'end': 13251.9, 'text': 'the science to it and the logic to it and the use of it for for inference for business logic and things like that will come a little later for now.', 'start': 13243.513, 'duration': 8.387}, {'end': 13259.446, 'text': 'we are simply describing then we are taken an even brief and perhaps even more confusing.', 'start': 13251.96, 'duration': 7.486}, {'end': 13265.972, 'text': 'look at multivariate our first multivariate summary where we looked at the idea for linear regression.', 'start': 13261.048, 'duration': 4.924}, {'end': 13274.377, 'text': 'a linear regression is an equation of the form.', 'start': 13272.216, 'duration': 2.161}], 'summary': 'Describing the science and logic behind linear regression for inference and business logic.', 'duration': 30.864, 'max_score': 13243.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY13243513.jpg'}, {'end': 13423.632, 'src': 'embed', 'start': 13394.303, 'weight': 0, 'content': [{'end': 13396.184, 'text': "so, for example, let's say that you're plotting.", 'start': 13394.303, 'duration': 1.881}, {'end': 13401.668, 'text': 'you can have, of course, one variable, as x one variable is y.', 'start': 13396.184, 'duration': 5.484}, {'end': 13405.07, 'text': 'another dimension can be maybe the size of the plot.', 'start': 13401.668, 'duration': 3.402}, {'end': 13409.844, 'text': 'you know, this is bigger than another variable z becomes larger.', 'start': 13406.442, 'duration': 3.402}, {'end': 13417.248, 'text': 'it can be a color like a heat map a fourth variable if it is low can be blue and if it is high can be red.', 'start': 13410.684, 'duration': 6.564}, {'end': 13423.632, 'text': 'another may be the shape of it lower values are circles higher values are more pointy.', 'start': 13418.629, 'duration': 5.003}], 'summary': 'Data visualization can involve multiple variables, such as x, y, size, color, and shape, to represent complex data relationships.', 'duration': 29.329, 'max_score': 13394.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY13394303.jpg'}, {'end': 13943.996, 'src': 'embed', 'start': 13912.47, 'weight': 10, 'content': [{'end': 13915.071, 'text': 'for every a and b i will get the value like this.', 'start': 13912.47, 'duration': 2.601}, {'end': 13919.732, 'text': 'if i take another line, i will get another value of this.', 'start': 13915.711, 'duration': 4.021}, {'end': 13926.974, 'text': 'for every choice of a and b i will get a difference distance from the data.', 'start': 13922.013, 'duration': 4.961}, {'end': 13936.55, 'text': 'which a and b will i pick? that a and b such that this distance becomes the smallest.', 'start': 13928.175, 'duration': 8.375}, {'end': 13941.694, 'text': 'so can we have an issue where the algorithm is computationally??', 'start': 13937.151, 'duration': 4.543}, {'end': 13943.996, 'text': "we don't find you, don't you?", 'start': 13941.714, 'duration': 2.282}], 'summary': 'Optimizing a and b to minimize distance, facing computational challenges.', 'duration': 31.526, 'max_score': 13912.47, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY13912470.jpg'}, {'end': 14258.406, 'src': 'embed', 'start': 14230.674, 'weight': 9, 'content': [{'end': 14234.517, 'text': 'so all these algorithms what they do is they take your prediction and they compare with the actuality.', 'start': 14230.674, 'duration': 3.843}, {'end': 14241.601, 'text': 'and they find a distance between them and they minimize the totality of the distance between the prediction, the actual,', 'start': 14235.319, 'duration': 6.282}, {'end': 14244.422, 'text': 'and algorithm that minimizes that distance is a good algorithm.', 'start': 14241.601, 'duration': 2.821}, {'end': 14245.462, 'text': 'it has learned well.', 'start': 14244.822, 'duration': 0.64}, {'end': 14258.406, 'text': 'so they all do something like this with this is the prediction and this is the actual and a and b are the parameters in the in the prediction.', 'start': 14248.063, 'duration': 10.343}], 'summary': 'Algorithms compare prediction with actuality to minimize distance and learn well.', 'duration': 27.732, 'max_score': 14230.674, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14230674.jpg'}, {'end': 14308.843, 'src': 'embed', 'start': 14276.733, 'weight': 1, 'content': [{'end': 14280.696, 'text': 'this is called least squares.', 'start': 14276.733, 'duration': 3.963}, {'end': 14291.953, 'text': "squares. here's a square least because you are minimizing this.", 'start': 14287.19, 'duration': 4.763}, {'end': 14296.275, 'text': 'call least squares and the least squares algorithm is a very standard way of doing things.', 'start': 14291.953, 'duration': 4.322}, {'end': 14299.057, 'text': 'this has nothing to do with the algorithm itself.', 'start': 14297.016, 'duration': 2.041}, {'end': 14303.96, 'text': 'the algorithm can be anything this itself can be a neural network.', 'start': 14300.638, 'duration': 3.322}, {'end': 14305.721, 'text': 'it can be a support vector machine.', 'start': 14304.38, 'duration': 1.341}, {'end': 14307.322, 'text': 'it can be a random forest.', 'start': 14306.101, 'duration': 1.221}, {'end': 14308.843, 'text': 'it can be association rule.', 'start': 14307.642, 'duration': 1.201}], 'summary': 'Least squares algorithm is a standard way of minimizing error, applicable to various algorithms like neural networks, support vector machines, random forests, and association rules.', 'duration': 32.11, 'max_score': 14276.733, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14276733.jpg'}], 'start': 11557.799, 'title': 'Statistical analysis techniques', 'summary': 'Discusses dimension reduction, heat maps, linear regression for predictive modeling, descriptive analysis, and multivariate analysis. it emphasizes the practical applications in fields like product sales analysis and gene expression levels.', 'chapters': [{'end': 11742.701, 'start': 11557.799, 'title': 'Dimension reduction and heat maps', 'summary': 'Discusses dimension reduction through the empirical relationship between height and weight, and the use of heat maps for visualizing correlations in large datasets, particularly in fields like product sales analysis and gene expression levels.', 'duration': 184.902, 'highlights': ['Dimension reduction involves combining two variables into one, relying on a nonlinear relationship, and is exemplified by the empirical relationship between height and weight in humans.', 'Heat maps serve as a visual tool for displaying correlations, particularly useful when analyzing large datasets such as product sales across geographies and gene expression levels, providing a clear visual representation of the data.', 'The correlation is limited in its analytical usefulness as it only captures linear relationships, and heat maps enhance this by adding colors to represent correlations, making it easier to interpret and analyze large datasets.']}, {'end': 12171.495, 'start': 11747.307, 'title': 'Linear regression for predictive modeling', 'summary': 'Covers the transition from descriptive to predictive modeling using linear regression, emphasizing the process of deriving a targeted equation for predictive purposes and its potential applications for multivariate analysis and predictive use cases.', 'duration': 424.188, 'highlights': ['Transition from descriptive to predictive modeling The chapter emphasizes the shift from descriptive to predictive modeling through the use of linear regression, showcasing the process of deriving a targeted equation for predictive purposes.', 'Potential applications for multivariate analysis The discussion highlights the potential applications of linear regression for multivariate analysis, demonstrating the use of multiple variables to describe relationships and the complexities of trivariate or multivariate analysis.', 'Predictive use cases of linear regression model The chapter delves into the predictive use cases of linear regression, explaining how the model can be utilized to predict outcomes based on new input values and make prescriptive recommendations for behavioral changes.']}, {'end': 13041.207, 'start': 12171.495, 'title': 'Linear regression and descriptive analysis', 'summary': 'Discusses linear regression, interpreting regression coefficients, the significance of the intercept, and the use of linear regression for descriptive analysis and prediction in supervised learning, with a focus on the relationship between the variables and the need for inferential testing.', 'duration': 869.712, 'highlights': ['The regression coefficients indicate that for every one unit increase in fitness, the miles increase by 27, and for every one unit increase in usage, the miles increase by 20. The interpretation of the regression coefficients reveals that a one unit increase in fitness leads to a 27 unit increase in miles, and a one unit increase in usage leads to a 20 unit increase in miles.', 'The intercept in the linear regression model is not treated as a coefficient and is purely descriptive, indicating that with zero usage and zero fitness, the miles are at -56, although this may not make practical sense. The intercept in the linear regression model is not considered a coefficient and purely describes the starting point, where with zero usage and fitness, the miles are at -56, which may not be practically meaningful.', 'The positive sign of the regression coefficients indicates that as fitness and usage increase, the miles also increase, demonstrating the descriptive use of linear regression to capture the relationship between variables. The positive sign of the regression coefficients signifies that as fitness and usage increase, the miles also increase, showcasing the descriptive use of linear regression to capture the relationship between variables.', 'The need for inferential testing and the significance of hypothesis testing to determine the predictive power of variables and the accuracy of the model, emphasizing the transition from descriptive to inferential analysis. Emphasizing the importance of inferential testing and hypothesis testing to assess the predictive power of variables and the accuracy of the model, highlighting the transition from descriptive to inferential analysis.', 'The chapter also emphasizes the distinction between descriptive statistics, predictive statistics, and prescriptive statistics, and the role of predictive analytics in machine learning and data mining. The distinction between descriptive statistics, predictive statistics, and prescriptive statistics is highlighted, along with the role of predictive analytics in machine learning and data mining.']}, {'end': 14462.667, 'start': 13042.808, 'title': 'Descriptive statistics and multivariate analysis', 'summary': 'Covers the concepts of descriptive statistics, including univariate data, measures of location, variation, and bivariate data, emphasizing the use of linear regression and visualization for summarizing relationships between variables.', 'duration': 1419.859, 'highlights': ['Descriptive Statistics and Univariate Data The chapter discusses univariate data and measures of location and variation, such as means, medians, quartiles, and standard deviation, as essential tools for conveying information about data distribution.', 'Bivariate Data and Linear Regression The chapter explores the concept of bivariate data and linear regression, emphasizing how linear regression is used to describe the relationship between variables and can be applied for prediction and prescription purposes.', 'Visualization Techniques for Data Analysis The chapter delves into the limitations of visualizing high-dimensional data and explores visualization methods such as histograms, box plots, and scatter plots, highlighting their role in aiding human perception of data distribution.', 'Least Squares Algorithm in Machine Learning The chapter introduces the concept of the least squares algorithm as a popular fitting algorithm used in machine learning to minimize the distance between predictions and actual data, enabling effective learning and prediction.', 'Training Data and Ground Truth in Machine Learning The chapter explains the significance of training data and ground truth in machine learning, emphasizing the role of providing data for teaching algorithms how to make correct decisions based on given situations.']}], 'duration': 2904.868, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY11557799.jpg', 'highlights': ['Heat maps visually display correlations in large datasets, aiding analysis.', 'Linear regression shifts from descriptive to predictive modeling, deriving targeted equations.', 'Multivariate analysis applies linear regression to describe complex relationships.', 'Linear regression predicts outcomes and prescribes behavioral changes based on new input.', 'Regression coefficients interpret the relationship between variables in linear regression.', 'Inferential testing assesses predictive power and model accuracy in linear regression.', 'Descriptive statistics convey data distribution using means, medians, and standard deviation.', 'Linear regression describes the relationship between variables in bivariate data.', 'Visualization methods like histograms and scatter plots aid in perceiving data distribution.', 'Least squares algorithm minimizes distance between predictions and actual data in machine learning.', 'Training data and ground truth are essential in teaching algorithms to make correct decisions.']}, {'end': 15300.384, 'segs': [{'end': 14514.588, 'src': 'embed', 'start': 14486.988, 'weight': 4, 'content': [{'end': 14490.47, 'text': "i'm not supposed to talk about this your your ml instructors are supposed to talk about this.", 'start': 14486.988, 'duration': 3.482}, {'end': 14492.491, 'text': "but suppose you're given a problem like this.", 'start': 14490.91, 'duration': 1.581}, {'end': 14499.174, 'text': "in other words, have given you a data set, and i'm going to tell you that your performance will be judged not by this data set,", 'start': 14493.251, 'duration': 5.923}, {'end': 14501.215, 'text': 'but on another data set that i am not giving you.', 'start': 14499.174, 'duration': 2.041}, {'end': 14503.876, 'text': 'how will you, what will you do??', 'start': 14502.835, 'duration': 1.041}, {'end': 14506.405, 'text': 'how will you make your program ready??', 'start': 14504.824, 'duration': 1.581}, {'end': 14511.147, 'text': 'yes, how will you make your program generalizable?', 'start': 14508.906, 'duration': 2.241}, {'end': 14514.588, 'text': "so the usual way it's done is something interesting.", 'start': 14512.827, 'duration': 1.761}], 'summary': 'How to make a program generalizable when judged on another data set.', 'duration': 27.6, 'max_score': 14486.988, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14486988.jpg'}, {'end': 14668.575, 'src': 'embed', 'start': 14637.45, 'weight': 1, 'content': [{'end': 14643.031, 'text': "so for example, you can have theories if we say for example, you could say let's say a savings rate.", 'start': 14637.45, 'duration': 5.581}, {'end': 14644.532, 'text': "what's the savings rate?", 'start': 14643.751, 'duration': 0.781}, {'end': 14648.372, 'text': 'of savings rate is the proportion of money that you save.', 'start': 14644.532, 'duration': 3.84}, {'end': 14656.634, 'text': 'now, if there is a saving state, what that would mean is that you, if i take your income data and i take your consumption data,', 'start': 14648.372, 'duration': 8.262}, {'end': 14657.654, 'text': 'that should form a straight line.', 'start': 14656.634, 'duration': 1.02}, {'end': 14668.575, 'text': "because you're saving up the same proportion every month, but it's not if you go home and month by month, you figured out what, what your income was,", 'start': 14659.254, 'duration': 9.321}], 'summary': 'Savings rate measures proportion of money saved, forming a straight line if consistent.', 'duration': 31.125, 'max_score': 14637.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14637450.jpg'}, {'end': 14706.868, 'src': 'embed', 'start': 14679.357, 'weight': 2, 'content': [{'end': 14683.298, 'text': 'it will have an increasing effect probably but it is very very unlikely to be a straight line.', 'start': 14679.357, 'duration': 3.941}, {'end': 14690.579, 'text': 'in certain things you may be going after a law of physics, but the law of physics may hold for gravity may not hold for anything else.', 'start': 14684.478, 'duration': 6.101}, {'end': 14693.523, 'text': 'i remember trying to apply this.', 'start': 14692.383, 'duration': 1.14}, {'end': 14699.025, 'text': 'so one day, one day cricket sort of became popular when i was in school or thereabouts,', 'start': 14693.563, 'duration': 5.462}, {'end': 14706.868, 'text': 'and one calculation was done as to has to how to figure out whether a team is doing well or how well is a chase going?', 'start': 14699.025, 'duration': 7.843}], 'summary': "The impact will increase, but not in a linear fashion. physics laws may not apply universally. cricket's popularity led to new performance metrics.", 'duration': 27.511, 'max_score': 14679.357, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14679357.jpg'}, {'end': 15063.155, 'src': 'embed', 'start': 15023.273, 'weight': 3, 'content': [{'end': 15030.356, 'text': 'this is this is my my summation y i minus a minus b x i whole squared.', 'start': 15023.273, 'duration': 7.083}, {'end': 15035.499, 'text': 'this value for different values of a and b i will get this.', 'start': 15031.757, 'duration': 3.742}, {'end': 15045.407, 'text': "so when i minimize this now to do this, you don't need to do it.", 'start': 15040.865, 'duration': 4.542}, {'end': 15053.611, 'text': 'all you need to do if you want to do it if you want to do it is this if you want to do if you want to do it do this.', 'start': 15045.567, 'duration': 8.044}, {'end': 15057.672, 'text': "do you have yesterday's code? open it.", 'start': 15054.191, 'duration': 3.481}, {'end': 15059.633, 'text': 'you can do it.', 'start': 15059.013, 'duration': 0.62}, {'end': 15062.675, 'text': 'then right now.', 'start': 15061.734, 'duration': 0.941}, {'end': 15063.155, 'text': "that's it.", 'start': 15062.815, 'duration': 0.34}], 'summary': 'Minimize the squared difference for different values of a and b to achieve the desired outcome.', 'duration': 39.882, 'max_score': 15023.273, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY15023273.jpg'}, {'end': 15313.834, 'src': 'embed', 'start': 15280.195, 'weight': 0, 'content': [{'end': 15283.056, 'text': 'is my demand elastic or is it inelastic?', 'start': 15280.195, 'duration': 2.861}, {'end': 15289.859, 'text': "if i want my prices to go up, then i want the demand to be inelastic, because i don't want my demand to go down.", 'start': 15283.056, 'duration': 6.803}, {'end': 15293.841, 'text': 'if i want my demand to, if i want my prices to go down.', 'start': 15291.14, 'duration': 2.701}, {'end': 15299.483, 'text': "but if i'm pulling my prices down, then i want the demand to be elastic, because i want people to say that your prices are going down.", 'start': 15295.06, 'duration': 4.423}, {'end': 15300.384, 'text': 'therefore i will buy more.', 'start': 15299.523, 'duration': 0.861}, {'end': 15304.907, 'text': 'so marketing analytics is very concerned with things like this.', 'start': 15302.545, 'duration': 2.362}, {'end': 15313.834, 'text': 'so therefore sometimes an equation of this kind is built just to describe something.', 'start': 15309.03, 'duration': 4.804}], 'summary': 'Demand elasticity influences pricing and purchasing decisions in marketing analytics.', 'duration': 33.639, 'max_score': 15280.195, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY15280195.jpg'}], 'start': 14462.687, 'title': 'Data validation and generalization', 'summary': 'Discusses the importance of data validation and generalization in building predictive models, including the process of test validate train, accurate performance measures, and complexities in estimating numerical values with examples like sentiment classification and savings rate.', 'chapters': [{'end': 14677.356, 'start': 14462.687, 'title': 'Data validation and generalization', 'summary': 'Discusses the importance of data validation and generalization in building predictive models, including the process of test validate train, the need for accurate performance measures, and the complexities in estimating numerical values with examples like classifying sentiment and savings rate.', 'duration': 214.669, 'highlights': ['The process of test validate train involves holding aside a portion of available data as validation data, building the algorithm on the remaining data, and testing it on the held-out data to assess generalizability.', 'In classifying sentiment, accuracy is measured by counting mistakes, while estimating numerical values requires a measure of proximity to the actual value, demonstrating the need for different performance measures in predictive modeling.', 'The complexities in estimating numerical values are exemplified by the savings rate, where the expected linear relationship between income and consumption data does not hold true, emphasizing the challenges in building predictive models for real-world phenomena.']}, {'end': 15300.384, 'start': 14679.357, 'title': 'Using physics in cricket and market analysis', 'summary': 'Explains how physics concepts were used in cricket analysis and how linear regression models are utilized in market analysis, demonstrating the applications and limitations of applying physical laws to non-physical scenarios, as well as the practical uses of linear regression models in determining price elasticity and market demand.', 'duration': 621.027, 'highlights': ['Explaining the application and limitations of using physical laws in non-physical scenarios such as cricket analysis and market demand measurement. The transcript discusses the application and limitations of using physical laws in non-physical scenarios, demonstrating how the principles of physics were applied to predict cricket performance and market demand measurement, emphasizing the constraints and approximations of such applications.', 'Describing the practical applications of linear regression models in determining price elasticity and market demand. The chapter elaborates on the practical uses of linear regression models, specifically in determining price elasticity and market demand, highlighting the importance of using such models as descriptors rather than predictors in market analysis.', 'Providing an example of using linear regression to measure price elasticity by calculating the elasticity of demand through the slope of the regression equation. An example is given on using linear regression to measure price elasticity by calculating the elasticity of demand through the slope of the regression equation, emphasizing the significance of using percentage changes and log scales to avoid dependency on units in market analysis.']}], 'duration': 837.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY14462687.jpg', 'highlights': ['The process of test validate train involves holding aside a portion of available data as validation data, building the algorithm on the remaining data, and testing it on the held-out data to assess generalizability.', 'In classifying sentiment, accuracy is measured by counting mistakes, while estimating numerical values requires a measure of proximity to the actual value, demonstrating the need for different performance measures in predictive modeling.', 'The complexities in estimating numerical values are exemplified by the savings rate, where the expected linear relationship between income and consumption data does not hold true, emphasizing the challenges in building predictive models for real-world phenomena.', 'Explaining the application and limitations of using physical laws in non-physical scenarios such as cricket analysis and market demand measurement.', 'Describing the practical applications of linear regression models in determining price elasticity and market demand.', 'Providing an example of using linear regression to measure price elasticity by calculating the elasticity of demand through the slope of the regression equation.']}, {'end': 16298.173, 'segs': [{'end': 16236.993, 'src': 'embed', 'start': 16207.149, 'weight': 0, 'content': [{'end': 16214.311, 'text': 'i remember running into trouble with my engineering friends on this working on the design of a fairly large aircraft engine.', 'start': 16207.149, 'duration': 7.162}, {'end': 16220.607, 'text': 'and there was a question of saying that you know what is the trust or what is the efficiency of the engine,', 'start': 16216.085, 'duration': 4.522}, {'end': 16224.068, 'text': "and i and i stupidly made the observation that why don't we test it out?", 'start': 16220.607, 'duration': 3.461}, {'end': 16229.39, 'text': 'and so they looked at me, this side, that side, etc.', 'start': 16226.049, 'duration': 3.341}, {'end': 16236.993, 'text': 'as if you know, how do we go to explain to this idiot? and then patiently one of them said to me very kindly.', 'start': 16229.57, 'duration': 7.423}], 'summary': 'Discussed testing aircraft engine efficiency and trust with engineering friends.', 'duration': 29.844, 'max_score': 16207.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16207149.jpg'}, {'end': 16298.173, 'src': 'embed', 'start': 16256.487, 'weight': 1, 'content': [{'end': 16263.853, 'text': 'pointing to the difficulty that i cannot easily do a full-blown test of a jet engine, because if i do start it,', 'start': 16256.487, 'duration': 7.366}, {'end': 16266.795, 'text': 'i got to give it enough room to move somewhere.', 'start': 16263.853, 'duration': 2.942}, {'end': 16275.642, 'text': 'so where do you want it to go? so where do you want it to go? so you will not be in a situation to do that very often.', 'start': 16268.556, 'duration': 7.086}, {'end': 16283.466, 'text': 'so when you say experiment it is sometimes your experiment and sometimes it is not in rare situations.', 'start': 16276.983, 'duration': 6.483}, {'end': 16287.168, 'text': 'will you be in an experimental see like in a be testing in websites? for example.', 'start': 16283.486, 'duration': 3.682}, {'end': 16293.831, 'text': "it's a common job marketing people often is asked to design websites and they are asked to say does is which is a good website.", 'start': 16288.068, 'duration': 5.763}, {'end': 16295.292, 'text': 'so you do an a b test.', 'start': 16294.572, 'duration': 0.72}, {'end': 16298.173, 'text': "what's in a b test you design a website of say type a or type b.", 'start': 16295.312, 'duration': 2.861}], 'summary': 'Challenges in conducting jet engine tests due to space constraints and need for experimental setups.', 'duration': 41.686, 'max_score': 16256.487, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16256487.jpg'}], 'start': 15302.545, 'title': 'Marketing analytics, covariance, regression, and probability fundamentals', 'summary': 'Covers building equations in marketing analytics with coefficients 36 and 22, manual calculation of covariance, regression analysis, emphasizing unit analysis, and probability fundamentals including empirical probability and real-world applications.', 'chapters': [{'end': 15383.992, 'start': 15302.545, 'title': 'Marketing analytics and equation building', 'summary': 'Discusses the use of equations in marketing analytics, with a focus on building an equation to describe the relationship between miles and usage, using coefficients 36 and 22.', 'duration': 81.447, 'highlights': ['An equation is built in marketing analytics to describe a relationship, such as the one between miles and usage.', 'The coefficients 36 and 22 are used in the equation to represent the relationship between miles and usage.']}, {'end': 15966.395, 'start': 15384.012, 'title': 'Covariance calculation and regression analysis', 'summary': 'Discusses manual calculation of covariance and regression analysis using a specific formula, providing an example with detailed steps and explanation, emphasizing the importance of unit analysis and the need for normalizing numbers in hypothesis testing.', 'duration': 582.383, 'highlights': ['The chapter discusses manual calculation of covariance and regression analysis using a specific formula. The transcript provides detailed steps on manually calculating covariance and regression analysis, emphasizing the specific formula and approach used.', 'Emphasizes the importance of unit analysis and the need for normalizing numbers in hypothesis testing. The importance of unit analysis and the need to normalize numbers in hypothesis testing is highlighted, emphasizing the significance of standard deviation in this process.', 'Provides an example with detailed steps and explanation. The transcript provides a detailed example of calculating covariance and regression analysis, including step-by-step explanation and reasoning behind the process.']}, {'end': 16298.173, 'start': 15967.276, 'title': 'Probability fundamentals', 'summary': 'Introduces the concept of probability by explaining uncertainty, empirical probability, experimental and observational studies, and the use of probability in various real-world scenarios such as marketing campaigns and nuclear tests.', 'duration': 330.897, 'highlights': ['The concept of probability is introduced by explaining uncertainty, empirical probability, and the distinction between experimental and observational studies. The transcript delves into the uncertainty associated with not knowing the true value of a population number and discusses empirical probability, which is determined by whether an event has occurred or not. It also distinguishes between experimental studies, where one designs the experiment, and observational studies, where the data is simply observed.', 'Real-world applications of probability are discussed, such as in marketing campaigns, portfolio design, product manufacturing, recruitment, and code testing. The transcript explains how probability is applied in various real-world scenarios, such as running a marketing campaign, designing a portfolio, manufacturing a product, recruiting people, and testing a piece of code.', 'The use of probability in nuclear testing and the challenges of conducting experimental studies in certain contexts are highlighted. The challenges of experimental studies are illustrated using examples like nuclear testing, where countries conduct experiments to collect data on the performance of nuclear devices. Additionally, the difficulties of conducting full-blown tests, such as testing a jet engine, are explained.']}], 'duration': 995.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY15302545.jpg', 'highlights': ['Real-world applications of probability are discussed, such as in marketing campaigns, portfolio design, product manufacturing, recruitment, and code testing.', 'The concept of probability is introduced by explaining uncertainty, empirical probability, and the distinction between experimental and observational studies.', 'The coefficients 36 and 22 are used in the equation to represent the relationship between miles and usage.', 'An equation is built in marketing analytics to describe a relationship, such as the one between miles and usage.', 'The chapter discusses manual calculation of covariance and regression analysis using a specific formula.', 'Emphasizes the importance of unit analysis and the need for normalizing numbers in hypothesis testing.']}, {'end': 17377.156, 'segs': [{'end': 16321.794, 'src': 'embed', 'start': 16298.173, 'weight': 0, 'content': [{'end': 16305.177, 'text': 'maybe one is the old website and you let them loose and you find out how people react to the different websites.', 'start': 16298.173, 'duration': 7.004}, {'end': 16308.486, 'text': 'this is a little tricky, but i want you to think about this.', 'start': 16306.144, 'duration': 2.342}, {'end': 16314.85, 'text': 'we will not spend a lot of time on it in manufacturing unit three parts of an assembly are selected.', 'start': 16308.626, 'duration': 6.224}, {'end': 16321.794, 'text': "we are observing whether they're defective or not defective determine the sample space and the event of getting at least two defective parts.", 'start': 16315.53, 'duration': 6.264}], 'summary': 'Testing two websites for user reaction; observing defective parts in an assembly.', 'duration': 23.621, 'max_score': 16298.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16298173.jpg'}, {'end': 16422.31, 'src': 'embed', 'start': 16394.741, 'weight': 3, 'content': [{'end': 16397.723, 'text': 'so this is a this is this is one way of describing the sample space.', 'start': 16394.741, 'duration': 2.982}, {'end': 16401.567, 'text': 'this is not the way the sample space is typically described.', 'start': 16399.004, 'duration': 2.563}, {'end': 16402.567, 'text': "you're not wrong.", 'start': 16401.907, 'duration': 0.66}, {'end': 16405.15, 'text': "but there's a problem.", 'start': 16404.389, 'duration': 0.761}, {'end': 16410.735, 'text': "and the problem is this so let's suppose i describe it this way.", 'start': 16406.471, 'duration': 4.264}, {'end': 16416.66, 'text': "in other words and now i describe my sample space as let's say zero defective.", 'start': 16412.016, 'duration': 4.644}, {'end': 16422.31, 'text': 'one defective to defective and three defective.', 'start': 16418.248, 'duration': 4.062}], 'summary': 'Describing sample space with 0, 1, 2, or 3 defectives.', 'duration': 27.569, 'max_score': 16394.741, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16394741.jpg'}, {'end': 16722.003, 'src': 'embed', 'start': 16698.067, 'weight': 5, 'content': [{'end': 16707.63, 'text': "what is the problem the event of getting at least two defective parts? in other words, i want to find probability of let's say two defectives.", 'start': 16698.067, 'duration': 9.563}, {'end': 16715.24, 'text': "what is this? let's work it out.", 'start': 16712.318, 'duration': 2.922}, {'end': 16716.64, 'text': "it's a good example to work out.", 'start': 16715.32, 'duration': 1.32}, {'end': 16718.461, 'text': "we'll understand many things as we do it.", 'start': 16716.861, 'duration': 1.6}, {'end': 16722.003, 'text': 'the chance of a single defect.', 'start': 16720.523, 'duration': 1.48}], 'summary': 'Calculating the probability of obtaining at least two defective parts', 'duration': 23.936, 'max_score': 16698.067, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16698067.jpg'}, {'end': 17063.594, 'src': 'embed', 'start': 17025.234, 'weight': 1, 'content': [{'end': 17030.457, 'text': 'this and i will now write as g into d sorry,', 'start': 17025.234, 'duration': 5.223}, {'end': 17036.471, 'text': 'into probability of d.', 'start': 17033.57, 'duration': 2.901}, {'end': 17039.832, 'text': "in other words, if there's an and then i can multiply.", 'start': 17036.471, 'duration': 3.361}, {'end': 17047.514, 'text': 'when if things are independent these laws will be clearly written later.', 'start': 17041.852, 'duration': 5.662}, {'end': 17049.954, 'text': 'so if things are independent.', 'start': 17048.414, 'duration': 1.54}, {'end': 17053.595, 'text': 'i can multiply if there is an end.', 'start': 17051.675, 'duration': 1.92}, {'end': 17056.696, 'text': 'if things are disjoint.', 'start': 17055.376, 'duration': 1.32}, {'end': 17060.117, 'text': 'then i can add when there is a or.', 'start': 17058.516, 'duration': 1.601}, {'end': 17063.594, 'text': 'common-sense rules.', 'start': 17062.813, 'duration': 0.781}], 'summary': "Explained rules of probability: multiplication for 'and', addition for 'or', and independence.", 'duration': 38.36, 'max_score': 17025.234, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY17025234.jpg'}], 'start': 16298.173, 'title': 'Probability in manufacturing units', 'summary': 'Discusses sample space, probability calculations for defective parts, and independence in probability, with examples such as a 10% probability of single defect and a 2.7% chance of success in a specific scenario.', 'chapters': [{'end': 16492.824, 'start': 16298.173, 'title': 'Probability in manufacturing units', 'summary': 'Discusses the concept of sample space and probability calculations in determining the likelihood of defective parts in a manufacturing unit, emphasizing the need to understand all possibilities before delving into probability calculations.', 'duration': 194.651, 'highlights': ['The importance of understanding sample space before probability calculations The chapter emphasizes the need to describe all possibilities (sample space) before delving into probability calculations in determining the likelihood of defective parts in a manufacturing unit.', 'Different ways of describing the sample space The transcript discusses different methods of describing the sample space, such as categorizing the possibilities based on the number of defective parts, with one method resulting in four possible outcomes.', 'Approach to probability calculations through splitting into individual components The chapter explains the approach to probability calculations, which involves splitting the sample space into individual events and then summing up the probabilities of each event to calculate the overall probability.']}, {'end': 16894.062, 'start': 16493.945, 'title': 'Defective parts probability', 'summary': 'Explores the concept of defining sample space based on defective parts, calculating probabilities of defective parts, and finding the chance of getting at least two defective parts in a set of three, using a 10% probability of single defect as an example.', 'duration': 400.117, 'highlights': ['The sample space is defined in terms of whether each individual item is actually defective or not, resulting in eight possibilities, making the calculation easier.', 'A 10% probability of a single defect is used as an example, leading to the calculation of chances of getting at least two defective parts in a set of three.', 'The probability of two defectives can happen in three ways: gbd, dgd, or ddg, which are mutually disjoint, allowing the calculation to be simplified by adding up their individual probabilities.', "The method of calculating the chance of three defectives involves adding up the chances of three mutually exclusive events, represented as p(gdd) + p(dgd) + p(ddg), where each event's probability is calculated by multiplying the probabilities of its individual components."]}, {'end': 17377.156, 'start': 16895.863, 'title': 'Independence and probability', 'summary': 'Discusses the concept of independence in probability, showcasing an example with a 10% chance of success, demonstrating the application of multiplication with independent events, and culminating in a binomial distribution calculation resulting in a 2.7% chance of success in a specific scenario.', 'duration': 481.293, 'highlights': ['The chapter explains the concept of independence in probability, highlighting that events being independent allows for the multiplication of probabilities, demonstrated through an example of a 10% chance of success resulting in a 1% chance of both events occurring.', 'It demonstrates the application of multiplication with independent events by showcasing the calculation of the probability of both events occurring, resulting in a 1% chance of success when there is a 10% chance for each event independently.', 'The chapter culminates in a detailed calculation using the binomial distribution, showcasing a scenario where the chance of success is 2.7% when attempting to sell a product to three people with a 10% chance of success.', 'It further elaborates on the concept of independence and probability by providing a real-world example of a salesperson attempting to sell to three people with a 10% chance of success.']}], 'duration': 1078.983, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY16298173.jpg', 'highlights': ['The chapter emphasizes the need to describe all possibilities (sample space) before delving into probability calculations.', 'The chapter explains the approach to probability calculations, which involves splitting the sample space into individual events and then summing up the probabilities of each event to calculate the overall probability.', 'The transcript discusses different methods of describing the sample space, such as categorizing the possibilities based on the number of defective parts, with one method resulting in four possible outcomes.', 'The sample space is defined in terms of whether each individual item is actually defective or not, resulting in eight possibilities, making the calculation easier.', 'The chapter explains the concept of independence in probability, highlighting that events being independent allows for the multiplication of probabilities, demonstrated through an example of a 10% chance of success resulting in a 1% chance of both events occurring.', 'The chapter culminates in a detailed calculation using the binomial distribution, showcasing a scenario where the chance of success is 2.7% when attempting to sell a product to three people with a 10% chance of success.']}, {'end': 18281.408, 'segs': [{'end': 17403.109, 'src': 'embed', 'start': 17378.517, 'weight': 0, 'content': [{'end': 17386.558, 'text': 'optimistically even 10%, which means that if i try to sell to 10 people, only one will probably by.', 'start': 17378.517, 'duration': 8.041}, {'end': 17389.94, 'text': 'so my chances of success for a single person is about 10%.', 'start': 17386.558, 'duration': 3.382}, {'end': 17394.624, 'text': 'so now i can ask myself what is going to happen at the end of today?', 'start': 17389.94, 'duration': 4.684}, {'end': 17396.145, 'text': 'how many will i sell??', 'start': 17395.264, 'duration': 0.881}, {'end': 17398.106, 'text': 'what is the chance that nobody will buy?', 'start': 17396.685, 'duration': 1.421}, {'end': 17400.568, 'text': 'what is the chance that one person will buy?', 'start': 17398.927, 'duration': 1.641}, {'end': 17403.109, 'text': 'what is the chance that two of them will buy?', 'start': 17401.408, 'duration': 1.701}], 'summary': 'The chance of selling to a single person is 10%, aiming to sell to multiple individuals.', 'duration': 24.592, 'max_score': 17378.517, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY17378517.jpg'}, {'end': 17469.8, 'src': 'embed', 'start': 17428.019, 'weight': 1, 'content': [{'end': 17433.223, 'text': "so which is i'm saying that this calculation doesn't depend upon whether your is defective whether is buying anything.", 'start': 17428.019, 'duration': 5.204}, {'end': 17438.592, 'text': "what it depends on is the probability of an event and you're asking the question.", 'start': 17434.07, 'duration': 4.522}, {'end': 17440.873, 'text': 'how many times will this happen?', 'start': 17438.972, 'duration': 1.901}, {'end': 17449.038, 'text': 'and that can be a defective part, that can be a seal, that can be the loss of value of a portfolio, that can be the attrition of a person,', 'start': 17440.873, 'duration': 8.165}, {'end': 17452.039, 'text': 'that can be a hit on a website, that can be a click-through rate.', 'start': 17449.038, 'duration': 3.001}, {'end': 17455.081, 'text': 'it can be a very small number for those of you who are in digital marketing.', 'start': 17452.059, 'duration': 3.022}, {'end': 17456.321, 'text': "what's a typical ctr??", 'start': 17455.301, 'duration': 1.02}, {'end': 17457.422, 'text': "what's a typical click-through rate??", 'start': 17456.341, 'duration': 1.081}, {'end': 17460.683, 'text': 'any of you in that industry??', 'start': 17459.683, 'duration': 1}, {'end': 17463.965, 'text': "so what's a typical click-through rate for you?", 'start': 17462.504, 'duration': 1.461}, {'end': 17466.479, 'text': 'website clicks.', 'start': 17465.899, 'duration': 0.58}, {'end': 17469.8, 'text': "how email is, but what's a click-through rate?", 'start': 17468.42, 'duration': 1.38}], 'summary': 'Calculation depends on event probability, applies to defective parts, portfolio value loss, attrition, website metrics.', 'duration': 41.781, 'max_score': 17428.019, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY17428019.jpg'}], 'start': 17378.517, 'title': 'Probability and sales outcomes', 'summary': 'Covers the probability of sales success, with a 10% success rate, and the analysis of potential sales outcomes based on different probabilities. it also discusses click-through rates (ctr) in digital marketing, highlighting a small percentage like 0.3% and the relationship between impressions, clicks, and achieving a certain number of clicks. additionally, it explains independent events in probability theory, the difference between mutually exclusive and independent events, and practical examples using playing cards.', 'chapters': [{'end': 17449.038, 'start': 17378.517, 'title': 'Probability of sales success', 'summary': 'Discusses the concept of sales success probability, highlighting a 10% success rate and the analysis of potential sales outcomes based on different probabilities of customer purchases.', 'duration': 70.521, 'highlights': ['The probability of success for a single person is about 10%.', "There's only about a two and a half percent chance that two out of three people will buy.", "The calculation doesn't depend on whether the customer is defective or buying anything, but rather on the probability of an event and the frequency of its occurrence."]}, {'end': 17845.149, 'start': 17449.038, 'title': 'Understanding click-through rates', 'summary': 'Explains the concept of click-through rates (ctr) in digital marketing, highlighting that ctr is typically a very small percentage, such as 0.3%, and discusses the relationship between impressions, clicks, and the probability of achieving a certain number of clicks based on the ctr.', 'duration': 396.111, 'highlights': ['Click-through rate (CTR) is typically a very small percentage, such as 0.3%. CTR is highlighted as a small percentage, providing a tangible example of 0.3%, illustrating the typical scale of CTR in digital marketing.', 'Discussion of the relationship between impressions, clicks, and the probability of achieving a certain number of clicks based on the CTR. The relationship between impressions, clicks, and the probability of achieving a certain number of clicks is explained, demonstrating the practical application of CTR in determining the expected number of clicks based on impressions and CTR.', 'Explanation of the concept of probability as a number between 0 and 1, often calculated as a ratio of favorable outcomes to total outcomes. The concept of probability is introduced, emphasizing it as a ratio between favorable outcomes and total outcomes, providing a foundational understanding of probability in the context of CTR calculations.']}, {'end': 18281.408, 'start': 17845.189, 'title': 'Probability theory and independent events', 'summary': 'Explains the concept of independent events in probability theory, emphasizing the difference between mutually exclusive and independent events and providing practical examples using playing cards. it also introduces the concept of disjoint events and explains how to calculate probabilities using the union and intersection operations from set theory.', 'duration': 436.219, 'highlights': ['The chapter emphasizes the difference between mutually exclusive and independent events in probability theory, highlighting that mutually exclusive events are not independent and provides practical examples using playing cards to illustrate the concept. Emphasis on the difference between mutually exclusive and independent events, practical examples using playing cards.', 'The concept of disjoint events and how to calculate probabilities using the union and intersection operations from set theory is introduced, with a detailed explanation of the probability calculation for scenarios involving drawing playing cards. Introduction of disjoint events, explanation of union and intersection operations from set theory, detailed probability calculation examples.']}], 'duration': 902.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY17378517.jpg', 'highlights': ['The probability of success for a single person is about 10%.', 'Click-through rate (CTR) is typically a very small percentage, such as 0.3%.', 'The chapter emphasizes the difference between mutually exclusive and independent events in probability theory.']}, {'end': 20958.984, 'segs': [{'end': 19167.291, 'src': 'embed', 'start': 19138.44, 'weight': 4, 'content': [{'end': 19142.823, 'text': 'and if it does have anything to do with income, then is high, better or is no better?', 'start': 19138.44, 'duration': 4.383}, {'end': 19143.263, 'text': "i don't know.", 'start': 19142.903, 'duration': 0.36}, {'end': 19146.485, 'text': "so what i've done is i've arranged my data in this particular way.", 'start': 19144.144, 'duration': 2.341}, {'end': 19149.446, 'text': 'and now is asking a few questions.', 'start': 19147.885, 'duration': 1.561}, {'end': 19156.648, 'text': 'what is the probability that a randomly selected person, or what is the probability someone is a buyer of a car??', 'start': 19150.646, 'duration': 6.002}, {'end': 19159.989, 'text': "it's, you don't even need to look at the full table.", 'start': 19157.628, 'duration': 2.361}, {'end': 19167.291, 'text': "this is 80 by 200 probability of let's say car.", 'start': 19161.949, 'duration': 5.342}], 'summary': 'Arranged data for analysis, discussing probability of car buyers.', 'duration': 28.851, 'max_score': 19138.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY19138440.jpg'}, {'end': 19795.914, 'src': 'embed', 'start': 19761.831, 'weight': 2, 'content': [{'end': 19763.052, 'text': 'what to do.', 'start': 19761.831, 'duration': 1.221}, {'end': 19768.413, 'text': 'probability of spam, which is an estimate of the proportion of emails that are spam or not spam.', 'start': 19763.052, 'duration': 5.361}, {'end': 19771.874, 'text': 'and probability of words.', 'start': 19769.853, 'duration': 2.021}, {'end': 19775.234, 'text': 'that has no conditioning in it.', 'start': 19773.774, 'duration': 1.46}, {'end': 19777.775, 'text': "this is what's usually called a lexicon.", 'start': 19776.075, 'duration': 1.7}, {'end': 19779.795, 'text': 'or a dictionary.', 'start': 19779.195, 'duration': 0.6}, {'end': 19786.577, 'text': 'so if you give me a dictionary of the language i can give you this denominator.', 'start': 19782.516, 'duration': 4.061}, {'end': 19795.914, 'text': 'if you give me shall we see an it estimate or a sociological estimate as to the proportion of words a proportion of emails that end up being spam.', 'start': 19788.089, 'duration': 7.825}], 'summary': 'The transcript discusses the probability of spam emails and the use of a lexicon to estimate the proportion of spam emails.', 'duration': 34.083, 'max_score': 19761.831, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY19761831.jpg'}, {'end': 19953.5, 'src': 'embed', 'start': 19893.342, 'weight': 0, 'content': [{'end': 19906.132, 'text': 'should i stop? in other words given cow should i stop? now think of the think of the problem that has to be solved to do that.', 'start': 19893.342, 'duration': 12.79}, {'end': 19907.033, 'text': 'i can flip it.', 'start': 19906.493, 'duration': 0.54}, {'end': 19913.278, 'text': 'now to flip it means what to flip it means essentially flip it by saying cow then stop essentially.', 'start': 19908.134, 'duration': 5.144}, {'end': 19914.599, 'text': 'i now have to tell the program.', 'start': 19913.298, 'duration': 1.301}, {'end': 19918.182, 'text': 'so i say stop given cow.', 'start': 19915.3, 'duration': 2.882}, {'end': 19923.066, 'text': 'so now i have to solve it by base serum cow given stop.', 'start': 19919.623, 'duration': 3.443}, {'end': 19934.596, 'text': 'so i need to say these are the situations in which a car has stopped and these are the situations in which a car has not stopped in a stock situation.', 'start': 19925.134, 'duration': 9.462}, {'end': 19938.957, 'text': 'look at what that car saw and in a not stop situation.', 'start': 19935.156, 'duration': 3.801}, {'end': 19940.597, 'text': 'look at what the car saw.', 'start': 19939.557, 'duration': 1.04}, {'end': 19950.319, 'text': 'like spam and not spare and now i can flip this and say therefore if this is what i saw i now know whether to stop or not.', 'start': 19942.838, 'duration': 7.481}, {'end': 19953.5, 'text': "it's a neat little logic.", 'start': 19952.58, 'duration': 0.92}], 'summary': 'Using logic and situations, determine when to stop a car.', 'duration': 60.158, 'max_score': 19893.342, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY19893342.jpg'}], 'start': 18281.408, 'title': 'Probability and machine learning', 'summary': 'Covers topics such as event probability, relationship between events, conditional probability, bayes theorem in machine learning, and probabilistic learning in decision making. it emphasizes the application of these concepts in analyzing data and making predictions, with examples from machine learning in action.', 'chapters': [{'end': 18456.368, 'start': 18281.408, 'title': 'Event probability and relationship', 'summary': 'Discusses the relationship between events, joint probability, and using unrelated events as information for analytic calculations, aiming to find the probability of a customer buying a product given their professional background.', 'duration': 174.96, 'highlights': ['The joint probability of events is of interest, such as the probability of a customer being an IT professional and buying the product, to calculate another event, like the likelihood of selling a product to an IT professional.', 'The calculation involves finding the probability of a customer buying the product given that they are an IT professional, applying the formula: P(A|B) = P(A and B) / P(B).', 'The concept of using unrelated events as information for analytic calculations, like determining the probability of an email being spam based on its content, is highlighted as a common practice in analytics.']}, {'end': 19455.694, 'start': 18457.028, 'title': 'Probability theory and set theory', 'summary': 'Discusses probability theory, set theory, and conditional probability, covering topics such as independent events, conditional probability, marginal probability, and joint probability, emphasizing the application of these concepts in analyzing data and making predictions.', 'duration': 998.666, 'highlights': ['The chance of doing well in both subjects is 35% when they are independent, and the chance of getting both draws as spades is 13/52 * 12/51. The chapter explains the multiplication rule for independent events, demonstrating that the chance of doing well in both subjects is 35% and the chance of getting both draws as spades is 13/52 * 12/51.', 'Conditional probability is illustrated through the example of the probability of a family buying a car given an income of 10 lakhs or above, which is 42/80 or 52.5%. The concept of conditional probability is exemplified by calculating the probability of a family buying a car given an income of 10 lakhs or above, resulting in a probability of 42/80 or 52.5%.', 'The chapter discusses the base theorem to calculate conditional probabilities using the joint and marginal probabilities, enabling better understanding of probability relationships. The base theorem is introduced to calculate conditional probabilities using joint and marginal probabilities, enhancing the comprehension of probability relationships.']}, {'end': 20102.534, 'start': 19457.654, 'title': 'Bayes theorem in machine learning', 'summary': 'Explains the application of bayes theorem in machine learning, particularly in the context of identifying spam emails, with an emphasis on the probability of words given spam and the implications for autonomous car decision-making.', 'duration': 644.88, 'highlights': ['Bayes Theorem application in identifying spam emails The chapter discusses using Bayes Theorem to determine the probability of spam given words, essential in building applications to identify spam emails based on email content.', 'Probability of words given spam for spam detection It emphasizes the importance of determining the probability of words given spam, which allows for the identification of spam based solely on the content of the email.', "Implications for autonomous car decision-making The implications of Bayes Theorem for autonomous car decision-making are highlighted, specifically in the context of determining when to stop based on what the car 'sees' on the road."]}, {'end': 20384.948, 'start': 20105.775, 'title': 'Probabilistic learning in decision making', 'summary': 'Discusses the challenges of case-based reasoning in handling all possible cases, and highlights the use of probabilistic methods in decision making, showcasing examples of conflicting outcomes and the dilemma of a computer in making decisions based on identical inputs with different outputs.', 'duration': 279.173, 'highlights': ['The challenge of case-based reasoning arises when it becomes difficult to enumerate all possible cases, as in the example of solving the spam problem for every conceivable word, illustrating the limitations of a full case-based approach.', 'The use of Bayesian methods and probabilistic learning allows for decision-making based on evidence, where the absence of certain words in an email is considered irrelevant and leads to no decision update.', 'The dilemma of conflicting outcomes is highlighted through examples such as two individuals with identical characteristics but differing actions, and test drivers facing the decision to stop or not stop with identical scenes, posing the question of how a computer should make decisions based on the same input leading to different outputs.', 'The discussion delves into the consideration of randomized responses and the potential disaster they could entail, emphasizing the need for safer alternatives in decision-making processes.', 'The chapter emphasizes the challenge of teaching a computer to have a sense of value similar to humans, exemplified by the scenario of two doctors interpreting the same medical report differently and the implications for an AI system for medicine.']}, {'end': 20958.984, 'start': 20385.409, 'title': 'Machine learning in action', 'summary': "Discusses the application of machine learning through examples of watson's decision-making process in jeopardy, the challenges of machine learning in real-world scenarios, and the evolution of deep learning algorithms, emphasizing the importance of context in algorithms.", 'duration': 573.575, 'highlights': ["Watson's decision-making process in Jeopardy Watson's decision-making process in Jeopardy, where it calculates probabilities to determine the likelihood of answers and the challenges it faces in Final Jeopardy, serves as a practical example of machine learning in action.", 'Challenges of machine learning in real-world scenarios The chapter highlights the complexity of machine learning in real-world scenarios due to the unpredictability of outputs for identical inputs, leading to the need for probabilistic approaches and the consideration of decision-making based on probabilities.', 'Evolution of deep learning algorithms The evolution of deep learning algorithms, including recurrent neural networks, has significantly improved the ability of computers to understand and utilize context, raising concerns and excitement about the potential of these advancements.']}], 'duration': 2677.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY18281408.jpg', 'highlights': ['The joint probability of events is of interest, such as the probability of a customer being an IT professional and buying the product, to calculate another event, like the likelihood of selling a product to an IT professional.', 'The chapter discusses using Bayes Theorem to determine the probability of spam given words, essential in building applications to identify spam emails based on email content.', 'The chance of doing well in both subjects is 35% when they are independent, and the chance of getting both draws as spades is 13/52 * 12/51.', "Watson's decision-making process in Jeopardy, where it calculates probabilities to determine the likelihood of answers and the challenges it faces in Final Jeopardy, serves as a practical example of machine learning in action.", 'The concept of using unrelated events as information for analytic calculations, like determining the probability of an email being spam based on its content, is highlighted as a common practice in analytics.']}, {'end': 21987.292, 'segs': [{'end': 21018.49, 'src': 'embed', 'start': 20987.566, 'weight': 1, 'content': [{'end': 20993.229, 'text': "so let's suppose that for whatever be the reason an hiv test gets done and the test turns out to be positive.", 'start': 20987.566, 'duration': 5.663}, {'end': 20996.904, 'text': "i hope it never happens to but let's suppose the test turns out to be positive.", 'start': 20994.363, 'duration': 2.541}, {'end': 21004.286, 'text': "the question is how scared should you be? very that's a reasonable answer.", 'start': 20997.704, 'duration': 6.582}, {'end': 21007.427, 'text': "but let's work it out.", 'start': 21006.626, 'duration': 0.801}, {'end': 21018.49, 'text': 'so to do that trying to calculate the probability of hiv given positive test.', 'start': 21008.907, 'duration': 9.583}], 'summary': 'Calculating probability of hiv given positive test result.', 'duration': 30.924, 'max_score': 20987.566, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY20987566.jpg'}, {'end': 21544.021, 'src': 'embed', 'start': 21510.626, 'weight': 0, 'content': [{'end': 21520.093, 'text': 'anyone else? 0.16 0.16 there is 16% chance you have hiv if you test positive.', 'start': 21510.626, 'duration': 9.467}, {'end': 21530.28, 'text': 'why is it that a fairly accurate test and 95% accurate test? my wife and i have a biotech company.', 'start': 21521.173, 'duration': 9.107}, {'end': 21534.002, 'text': "we're trying to release a product on molecular diagnosis for infectious diseases.", 'start': 21530.3, 'duration': 3.702}, {'end': 21538.245, 'text': "if we get 95% we'd be thrilled our investors would be thrilled.", 'start': 21535.023, 'duration': 3.222}, {'end': 21538.906, 'text': "we'd be in business.", 'start': 21538.285, 'duration': 0.621}, {'end': 21544.021, 'text': 'this is not easy to attain particularly cheap.', 'start': 21541.159, 'duration': 2.862}], 'summary': 'A biotech company aims for 95% accuracy in molecular diagnosis for infectious diseases, which would thrill investors and enable them to launch their product.', 'duration': 33.395, 'max_score': 21510.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY21510626.jpg'}], 'start': 20960.724, 'title': 'Probability and risk in hiv testing', 'summary': 'Discusses the calculation of hiv probability given a positive test, emphasizes understanding probability in decision-making, demonstrates accuracy calculations using sensitivity and specificity, and explains bayes theorem with examples, highlighting the importance of independent tests in reducing false positives.', 'chapters': [{'end': 21018.49, 'start': 20960.724, 'title': 'Probability calculation and risk assessment', 'summary': 'Discusses the calculation of the probability of hiv given a positive test and challenges the perception of risk in various scenarios, emphasizing the importance of understanding probability in decision-making.', 'duration': 57.766, 'highlights': ['The chapter challenges the perception of risk by discussing the probability calculation of HIV given a positive test, emphasizing the importance of understanding probability in decision-making.', 'The speaker mentions the routine nature of HIV tests, indicating their prevalence and regularity in various medical procedures and surgeries.', 'The chapter highlights the hypothetical scenario of receiving a positive HIV test result and discusses the appropriate level of fear, emphasizing the need to work out the probability.', "The speaker raises the question of how scared one should be upon receiving a positive HIV test result, indicating the reasonable answer of 'very' and prompting a calculation of the probability of HIV given a positive test."]}, {'end': 21571.76, 'start': 21020.01, 'title': 'Calculating hiv test accuracy', 'summary': 'Discusses the calculation of the accuracy of an hiv test, demonstrating the use of sensitivity and specificity numbers to determine the probability of having hiv given a positive test result, with example calculations showing a 16% chance of having hiv if testing positive with a 95% accurate test.', 'duration': 551.75, 'highlights': ['The chapter discusses the calculation of the accuracy of an HIV test, demonstrating the use of sensitivity and specificity numbers to determine the probability of having HIV given a positive test result, with example calculations showing a 16% chance of having HIV if testing positive with a 95% accurate test.', 'The formula for calculating the probability of having HIV given a positive test result involves the sensitivity of the test (e.g., 95%) and the incidence rate of HIV (e.g., 1%), resulting in a 16% chance of having HIV if testing positive with a 95% accurate test.', 'The discussion highlights the significance of test accuracy, with a 95% accurate test demonstrating a 16% chance of having HIV if testing positive, showcasing the importance of achieving high accuracy in diagnostic tests for infectious diseases.']}, {'end': 21987.292, 'start': 21572.16, 'title': 'Bayes theorem and false positive', 'summary': 'Explains bayes theorem using an example of testing for hiv, highlighting how false positives can occur and the importance of independent tests, showing the chance of false positives becoming much lower when testing twice in a row.', 'duration': 415.132, 'highlights': ['The chance of a false positive becomes much lower when testing twice in a row, with the probability decreasing to a quarter of a percent or even less, emphasizing the importance of independent tests to reduce the likelihood of false positives.', 'The chapter demonstrates the application of Bayes Theorem in testing for fraud, highlighting the need for independent tests to avoid misidentifying non-fraudulent transactions as fraudulent, illustrating the importance of fresh data in machine learning situations.', 'The explanation of Bayes Theorem uses the example of testing for HIV, where the calculation shows that a large number of false positives occur due to a significant number of healthy people testing positive, emphasizing the need for retesting to reduce the chance of false positives.']}], 'duration': 1026.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY20960724.jpg', 'highlights': ['The chapter challenges the perception of risk by discussing the probability calculation of HIV given a positive test, emphasizing the importance of understanding probability in decision-making.', 'The chance of a false positive becomes much lower when testing twice in a row, with the probability decreasing to a quarter of a percent or even less, emphasizing the importance of independent tests to reduce the likelihood of false positives.', 'The chapter discusses the calculation of the accuracy of an HIV test, demonstrating the use of sensitivity and specificity numbers to determine the probability of having HIV given a positive test result, with example calculations showing a 16% chance of having HIV if testing positive with a 95% accurate test.']}, {'end': 23366.824, 'segs': [{'end': 22421.246, 'src': 'embed', 'start': 22382.385, 'weight': 1, 'content': [{'end': 22390.169, 'text': 'what is 35% of 70,000? 24,500 and so what is my answer 22,500 divided by 22,500 plus 24,500 which is presumably my 47% you can do this as well.', 'start': 22382.385, 'duration': 7.784}, {'end': 22391.75, 'text': 'so without opening the email.', 'start': 22390.189, 'duration': 1.561}, {'end': 22415.645, 'text': 'without opening the email and seeing the email, the chance that it is spam is 30%.', 'start': 22409.824, 'duration': 5.821}, {'end': 22421.246, 'text': 'but if the word congratulation is there in the email, the chance that it is spam has gone up to 47%.', 'start': 22415.645, 'duration': 5.601}], 'summary': "Calculation shows 35% of 70,000 is 24,500. chance of email being spam increases to 47% with the word 'congratulation.'", 'duration': 38.861, 'max_score': 22382.385, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY22382385.jpg'}, {'end': 22891.102, 'src': 'embed', 'start': 22820.931, 'weight': 0, 'content': [{'end': 22824.952, 'text': 'if a sample of seven accounts is selected at random from the current database,', 'start': 22820.931, 'duration': 4.021}, {'end': 22828.033, 'text': 'construct the binomial probability distribution of accounts paying on time.', 'start': 22824.952, 'duration': 3.081}, {'end': 22835.035, 'text': 'what is the question being asked the question being asked is this that i am looking at seven accounts.', 'start': 22828.953, 'duration': 6.082}, {'end': 22842.933, 'text': "and i'm trying to understand how many of those accounts? are paying up?", 'start': 22836.65, 'duration': 6.283}, {'end': 22847.596, 'text': 'how many of those accounts are paying up now?', 'start': 22844.814, 'duration': 2.782}, {'end': 22849.497, 'text': 'what values can it take??', 'start': 22847.876, 'duration': 1.621}, {'end': 22855.66, 'text': 'what? what are the possible values that that my ex can take?', 'start': 22851.938, 'duration': 3.722}, {'end': 22867.463, 'text': '0, 1, 2, 3, 4, 5, 6 and 7?', 'start': 22855.66, 'duration': 11.803}, {'end': 22870.986, 'text': 'six means none pay on time.', 'start': 22867.463, 'duration': 3.523}, {'end': 22879.633, 'text': "i'm sorry zero means none pay on time one means one pays on time seven means all pay on time.", 'start': 22873.288, 'duration': 6.345}, {'end': 22891.102, 'text': "the chance that every one of them individually pay on time is 60% and i'm going to make the assumption that these people aren't talking to each other.", 'start': 22881.555, 'duration': 9.547}], 'summary': 'Construct binomial probability distribution for 7 accounts paying on time, with 60% individual payment probability.', 'duration': 70.171, 'max_score': 22820.931, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY22820931.jpg'}], 'start': 21987.812, 'title': 'Probability and text mining', 'summary': "Discusses the calculation of spam probability based on word presence, with an example of 'congratulation' increasing spam chance to 47%. it also explains text mining, joint and conditional probabilities, and binomial distribution in spam detection, defective products, and customer service. additionally, it covers the construction of a binomial probability distribution for a bank's accounts, with applications in sales team budgeting and optimization.", 'chapters': [{'end': 22421.246, 'start': 21987.812, 'title': 'Probability of spam', 'summary': "Discusses the calculation of the probability of an email being spam given the presence of a specific word, using a scenario where the chance of an email being spam increases to 47% if the word 'congratulation' is present.", 'duration': 433.434, 'highlights': ['The probability of an email being spam is 30%.', "The probability of an email being spam increases to 47% if the word 'congratulation' is present.", "Calculation of the probability of an email being spam given the presence of the word 'congratulation' using the formula and a graphical representation."]}, {'end': 22800.874, 'start': 22421.246, 'title': 'Text mining and probabilistic models', 'summary': 'Explains the use of bag of words approach in text mining, the concept of joint and conditional probabilities in the context of spam detection, and the application of binomial distribution in counting defective products or customer service.', 'duration': 379.628, 'highlights': ['The bag of words approach in text mining involves putting words into a bag irrespective of their order and calculating the probability of word occurrences in an email, assuming independence.', 'The concept of joint and conditional probabilities is applied to spam detection, where the probability of spam given certain words is calculated by multiplying the individual probabilities of each word given spam.', 'The application of binomial distribution is illustrated, where the probability of getting x successes out of n trials is calculated using the formula n choose x * p^x * (1-p)^(n-x), with examples of counting defective products or customer service.', 'The explanation of trials and probability of success in a single trial, where the probability of getting x successes out of n trials is calculated using the formula n choose x * p^x * (1-p)^(n-x), is discussed with an example of counting defective products or customer service.']}, {'end': 23366.824, 'start': 22802.115, 'title': 'Binomial probability distribution', 'summary': "Explains the construction of a binomial probability distribution for a bank's accounts paying on time, with a 60% chance of payment, and explores its applications in sales team budgeting and optimization, considering the total number of trials, possible outcomes, and the probability of making more than three sales in a day.", 'duration': 564.709, 'highlights': ['Constructing the binomial probability distribution of accounts paying on time The bank has found that 60% of all accounts pay on time, and the chapter explains the calculation of the probability distribution for the number of accounts paying on time when a sample of seven accounts is selected at random from the current database.', 'Calculation of the probability for a specific scenario The calculation of the probability that two out of seven accounts pay on time is demonstrated using the binomial probability formula, resulting in 21 possible arrangements for two accounts paying on time out of seven.', 'Application of binomial distribution in sales team budgeting The chapter presents an application of the binomial distribution in determining the probability of making more than three sales in a day, which can be used to optimize the size of a sales team and budget allocation.']}], 'duration': 1379.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY21987812.jpg', 'highlights': ["The probability of an email being spam increases to 47% if the word 'congratulation' is present.", 'The bag of words approach in text mining involves putting words into a bag irrespective of their order and calculating the probability of word occurrences in an email, assuming independence.', 'The application of binomial distribution is illustrated, where the probability of getting x successes out of n trials is calculated using the formula n choose x * p^x * (1-p)^(n-x), with examples of counting defective products or customer service.', 'Constructing the binomial probability distribution of accounts paying on time The bank has found that 60% of all accounts pay on time, and the chapter explains the calculation of the probability distribution for the number of accounts paying on time when a sample of seven accounts is selected at random from the current database.']}, {'end': 24674.986, 'segs': [{'end': 23911.191, 'src': 'embed', 'start': 23880.936, 'weight': 0, 'content': [{'end': 23887.779, 'text': 'so the p comes from the data, but the calculation for saying how many people will pay their bills on time comes from the next month.', 'start': 23880.936, 'duration': 6.843}, {'end': 23889.72, 'text': 'it is done for the next month.', 'start': 23888.6, 'duration': 1.12}, {'end': 23894.703, 'text': 'it makes no sense to do it for this month because i already have this month exactly.', 'start': 23890.561, 'duration': 4.142}, {'end': 23901.666, 'text': "but let's take a situation that the probability that we added right probability of 1% probability of two percent.", 'start': 23894.723, 'duration': 6.943}, {'end': 23902.707, 'text': 'yes, exactly.', 'start': 23901.686, 'duration': 1.021}, {'end': 23911.191, 'text': 'yes it already has because the p has come from the past data.', 'start': 23902.907, 'duration': 8.284}], 'summary': 'Calculating bill payment probability for next month based on past data.', 'duration': 30.255, 'max_score': 23880.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY23880936.jpg'}, {'end': 24340.066, 'src': 'embed', 'start': 24288.017, 'weight': 3, 'content': [{'end': 24293.519, 'text': 'this is what 2.4 into 10 to the power minus 3.', 'start': 24288.017, 'duration': 5.502}, {'end': 24301.041, 'text': "so this is what point zero zero two? let's see what happens.", 'start': 24293.519, 'duration': 7.522}, {'end': 24313.495, 'text': 'so what is the probability of zero point zero zero two? what is it for one? point zero zero one for two but 3.008 for 4.', 'start': 24301.681, 'duration': 11.814}, {'end': 24318.279, 'text': 'no, what is it for? what is it for forces this 0? 1 2 3 4.', 'start': 24313.495, 'duration': 4.784}, {'end': 24318.75, 'text': 'what is it for 4.13.', 'start': 24318.279, 'duration': 0.471}, {'end': 24319.481, 'text': '13%. what is it for 5? 16%?', 'start': 24318.75, 'duration': 0.731}, {'end': 24319.781, 'text': 'was it?', 'start': 24319.481, 'duration': 0.3}, {'end': 24340.066, 'text': 'what is it for 6? 16%?', 'start': 24319.781, 'duration': 20.285}], 'summary': 'Probability percentage for different values: 2.4x10^-3, 3.008, 13%, 16%.', 'duration': 52.049, 'max_score': 24288.017, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY24288017.jpg'}, {'end': 24513.403, 'src': 'embed', 'start': 24468.584, 'weight': 1, 'content': [{'end': 24469.885, 'text': 'now the normal distribution.', 'start': 24468.584, 'duration': 1.301}, {'end': 24472.888, 'text': 'the reason i wanted to get to is this because because of this picture.', 'start': 24470.065, 'duration': 2.823}, {'end': 24477.672, 'text': 'now this picture puts the standard deviation in context.', 'start': 24474.11, 'duration': 3.562}, {'end': 24482.936, 'text': 'so yesterday we talked about the standard deviation and a question often asked is what does the standard deviation mean?', 'start': 24477.893, 'duration': 5.043}, {'end': 24484.577, 'text': 'what is standard about the standard deviation?', 'start': 24482.996, 'duration': 1.581}, {'end': 24488.239, 'text': 'this picture tells you what is standard about the standard deviation.', 'start': 24485.197, 'duration': 3.042}, {'end': 24502.129, 'text': 'so this picture means that if i have a normal distribution then the chance of being within one standard deviation is 68% as a numerical quantity.', 'start': 24489.18, 'duration': 12.949}, {'end': 24506.441, 'text': 'this distribution is a distribution that has a mean.', 'start': 24503.22, 'duration': 3.221}, {'end': 24509.021, 'text': 'and it has a standard deviation.', 'start': 24507.781, 'duration': 1.24}, {'end': 24513.403, 'text': 'now the standard deviation has to be defined in such a way,', 'start': 24510.042, 'duration': 3.361}], 'summary': 'The normal distribution shows 68% chance within 1 standard deviation.', 'duration': 44.819, 'max_score': 24468.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY24468584.jpg'}], 'start': 23371.949, 'title': 'Probability distributions in business analysis', 'summary': 'Covers the application of binomial distribution in contact centers to calculate escalations, probability distribution in business for on-time payments and expected revenue, optimizing collection process with mathematical models to reduce late payments to less than 0.1%, poisson distribution for customer arrival rates, and understanding normal distribution and standard deviation with practical application examples.', 'chapters': [{'end': 23509.659, 'start': 23371.949, 'title': 'Binomial distribution in contact centers', 'summary': 'Discusses the application of binomial distribution in contact centers to calculate escalations and demonstrates the use of the binomial stats dot binomial dot pmf command to calculate the probability mass function for a given scenario.', 'duration': 137.71, 'highlights': ['The binomial distribution is used in contact centers to calculate the expected number of escalations, with a demonstration of using the binomial stats dot binomial dot pmf command to calculate the probability mass function.', "The command 'binomial stats dot binomial dot pmf' is used to calculate the probability mass function, representing the distribution of probability across different numbers in a scenario.", "The formula for binomial distribution, 'n choose k', is calculated using the command, and manual calculation is also explained for a specific scenario of 21 into 0.6 to the power 2, into 0.5 to the power 4."]}, {'end': 23927.939, 'start': 23510.419, 'title': 'Probability distribution in business', 'summary': 'Discusses the probability distribution in the business context, where it analyzes the likelihood of on-time payments in a business, highlighting the distribution percentages and the calculation of expected revenue based on the probability.', 'duration': 417.52, 'highlights': ['The chance of four people paying on time is 29%, while the chance of five people paying on time is 26%.', 'The average of a binomial distribution is given by the total number of trials multiplied by the probability, such as 7 multiplied by 0.6, resulting in an expected 4.2 people paying on time.', 'The probability of on-time payments can be computed based on past data, and it is essential for businesses to estimate their expected revenue per month using the average of the binomial distribution.']}, {'end': 24085.734, 'start': 23929.219, 'title': 'Optimizing collection process with mathematical models', 'summary': 'Discusses the use of mathematical models to optimize the collection process, aiming to increase the percentage of people paying bills on time and reduce the number of late payments to less than 0.1% by adjusting the variable p.', 'duration': 156.515, 'highlights': ['The goal is to increase the percentage of people paying bills on time from 60% to a level where the number of people not paying on time is less than 0.1% by adjusting the variable p. The current situation indicates that 60% of people pay their bills on time, with a goal to reduce the number of late payments to less than 0.1% by adjusting the variable p.', 'The mathematical model involves adjusting the variable p to achieve the target percentage of on-time bill payments, with the flexibility to create applications in various ways to attain the desired p. The model involves setting a target p to achieve the desired percentage of on-time bill payments, offering flexibility in creating applications to reach the specified target p.', 'The Poisson distribution is highlighted as a similar distribution to the binomial distribution, with the distinction that it does not have a maximum count and is suitable for scenarios where a maximum does not make sense, such as counting fraud cases or micro fractures. The Poisson distribution is discussed as an alternative to the binomial distribution, particularly suitable for scenarios where a maximum count is not applicable, like counting fraud cases or micro fractures.']}, {'end': 24442.136, 'start': 24086.194, 'title': 'Poisson distribution in customer arrival', 'summary': 'Discusses the poisson distribution in the context of customer arrival rates, specifically addressing the probability of a given number of customers arriving in a minute, with an average arrival rate of 6 customers every two minutes.', 'duration': 355.942, 'highlights': ['The formula for the Poisson distribution involves a single rate parameter, representing the average arrival rate, such as 6 customers every two minutes. The Poisson distribution formula involves a single rate parameter, which in this context represents the average arrival rate of 6 customers every two minutes.', 'Calculating the probability of exactly four customers arriving in a minute yields a value of 0.0024, or 0.24%. The probability of exactly four customers arriving in a minute, with an average arrival rate of 6 customers every two minutes, is calculated to be 0.0024, equivalent to 0.24%.', 'The probability of more than three customers arriving in a given minute is demonstrated to be over 60%. The probability of more than three customers arriving in a given minute, with an average arrival rate of 6 customers every two minutes, is demonstrated to be over 60%.']}, {'end': 24674.986, 'start': 24442.136, 'title': 'Understanding normal distribution and standard deviation', 'summary': 'Provides an explanation of normal distribution and standard deviation, emphasizing that 68% of the data falls within one standard deviation, 95% within two standard deviations, and 99.3% within three standard deviations, illustrating the practical application of these concepts through the example of mean height and standard deviation.', 'duration': 232.85, 'highlights': ['The standard deviation implies that 68% of the data falls within one standard deviation, 95% within two standard deviations, and 99.3% within three standard deviations, providing a clear understanding of the dispersion of data. Explains the practical implications of standard deviation in a normal distribution, illustrating that 68% of data falls within one standard deviation, 95% within two standard deviations, and 99.3% within three standard deviations.', 'The example of mean height and standard deviation demonstrates the practical application of these concepts, showcasing how the mean and standard deviation provide valuable information about the spread of data. Illustrates the practical application of mean and standard deviation by providing an example of mean height and standard deviation, showcasing how this information can effectively describe the distribution of data.', 'The chapter emphasizes that the mean and standard deviation can provide valuable insights into the spread of data, serving as crucial information even when the data is not directly available. Highlights the importance of mean and standard deviation in understanding data dispersion, especially when direct data is unavailable, emphasizing their role in providing valuable insights into data spread.']}], 'duration': 1303.037, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY23371949.jpg', 'highlights': ['The binomial distribution is used in contact centers to calculate the expected number of escalations, with a demonstration of using the binomial stats dot binomial dot pmf command to calculate the probability mass function.', 'The chance of four people paying on time is 29%, while the chance of five people paying on time is 26%.', 'The goal is to increase the percentage of people paying bills on time from 60% to a level where the number of people not paying on time is less than 0.1% by adjusting the variable p.', 'The Poisson distribution is highlighted as a similar distribution to the binomial distribution, with the distinction that it does not have a maximum count and is suitable for scenarios where a maximum does not make sense, such as counting fraud cases or micro fractures.', 'The formula for the Poisson distribution involves a single rate parameter, representing the average arrival rate, such as 6 customers every two minutes.', 'The standard deviation implies that 68% of the data falls within one standard deviation, 95% within two standard deviations, and 99.3% within three standard deviations, providing a clear understanding of the dispersion of data.']}, {'end': 25962.083, 'segs': [{'end': 25069.245, 'src': 'embed', 'start': 25000.04, 'weight': 1, 'content': [{'end': 25005.502, 'text': "do you understand how the code works? let's do the second problem.", 'start': 25000.04, 'duration': 5.462}, {'end': 25011.685, 'text': 'what is the probability that the pack weighs more than 350 grams?', 'start': 25007.083, 'duration': 4.602}, {'end': 25013.065, 'text': 'what do you think?', 'start': 25012.625, 'duration': 0.44}, {'end': 25014.546, 'text': 'the answer should be guess?', 'start': 25013.065, 'duration': 1.481}, {'end': 25018.507, 'text': 'yes, one minus what?', 'start': 25016.887, 'duration': 1.62}, {'end': 25024.809, 'text': 'one minus stats dot norm.', 'start': 25022.369, 'duration': 2.44}, {'end': 25028.691, 'text': 'now, what should i do? sorry norm dot cdf.', 'start': 25025.29, 'duration': 3.401}, {'end': 25036.487, 'text': 'point three five zero comma same thing.', 'start': 25032.264, 'duration': 4.223}, {'end': 25069.245, 'text': 'about one point three nine percent the chance of being more than 380 clear.', 'start': 25056.124, 'duration': 13.121}], 'summary': 'Probability of pack weighing more than 350g is about 1.39%.', 'duration': 69.205, 'max_score': 25000.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25000040.jpg'}, {'end': 25206.401, 'src': 'embed', 'start': 25178.44, 'weight': 10, 'content': [{'end': 25182.764, 'text': 'what numbers am i using? mean and standard deviation.', 'start': 25178.44, 'duration': 4.324}, {'end': 25187.874, 'text': "so what i'm doing is what is the advantage that i have? i don't need the data.", 'start': 25184.065, 'duration': 3.809}, {'end': 25189.735, 'text': 'all i need is this mean and standard deviation.', 'start': 25188.054, 'duration': 1.681}, {'end': 25193.756, 'text': 'what is the price i pay? an assumption on the distribution.', 'start': 25190.115, 'duration': 3.641}, {'end': 25203.44, 'text': 'no, so the i could instead of using norm have another distribution sitting there.', 'start': 25197.618, 'duration': 5.822}, {'end': 25206.401, 'text': "there's a whole range other other possibilities.", 'start': 25204.18, 'duration': 2.221}], 'summary': 'Analyzing data using mean and standard deviation, considering distribution assumptions for possible alternatives.', 'duration': 27.961, 'max_score': 25178.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25178440.jpg'}, {'end': 25439.384, 'src': 'embed', 'start': 25409.817, 'weight': 9, 'content': [{'end': 25410.897, 'text': 'is it a data question??', 'start': 25409.817, 'duration': 1.08}, {'end': 25414.9, 'text': 'how do you reach the customer??', 'start': 25412.959, 'duration': 1.941}, {'end': 25417.582, 'text': 'yes, and what kind of packaging sizes?', 'start': 25415.2, 'duration': 2.382}, {'end': 25419.103, 'text': 'so what, what data is that??', 'start': 25417.882, 'duration': 1.221}, {'end': 25427.721, 'text': 'so data of what data?', 'start': 25424.7, 'duration': 3.021}, {'end': 25428.621, 'text': "my sku's?", 'start': 25427.721, 'duration': 0.9}, {'end': 25431.802, 'text': 'how many data observations, which data observations for whom?', 'start': 25428.621, 'duration': 3.181}, {'end': 25434.062, 'text': 'for which customer, when what data?', 'start': 25431.802, 'duration': 2.26}, {'end': 25439.384, 'text': 'so kilos quality check for what.', 'start': 25436.963, 'duration': 2.421}], 'summary': 'Discussion on customer reach, packaging sizes, and data observations.', 'duration': 29.567, 'max_score': 25409.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25409817.jpg'}, {'end': 25560.761, 'src': 'embed', 'start': 25531.817, 'weight': 8, 'content': [{'end': 25537.578, 'text': "i'm interested in seeing all there is a tech question that i'm interested in answering and often that is made independently of the data.", 'start': 25531.817, 'duration': 5.761}, {'end': 25542.578, 'text': 'so for example, the car has to stop autonomous vehicles.', 'start': 25539.997, 'duration': 2.581}, {'end': 25549.299, 'text': 'take the data that the car is going to react to is the scene that the car sees in front of it.', 'start': 25545.058, 'duration': 4.241}, {'end': 25553.02, 'text': "but that's not the data on which the algorithm is going to be based.", 'start': 25550.559, 'duration': 2.461}, {'end': 25560.761, 'text': 'so the so the data that the car sees is what it is reacting to similarly.', 'start': 25555.42, 'duration': 5.341}], 'summary': 'Tech questions often answered independently of data, e.g., autonomous cars react to scene data.', 'duration': 28.944, 'max_score': 25531.817, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25531817.jpg'}, {'end': 25738.929, 'src': 'embed', 'start': 25715.903, 'weight': 7, 'content': [{'end': 25728.071, 'text': "is the power of probability that you're being able to answer a question like saying do i expect that the weight is going to be less than 280 grams? without having data in place for it?", 'start': 25715.903, 'duration': 12.168}, {'end': 25733.687, 'text': 'the simpler answer would be give me the data and count how many are less than 280 grams.', 'start': 25729.326, 'duration': 4.361}, {'end': 25734.667, 'text': "that's the simplest answer.", 'start': 25733.707, 'duration': 0.96}, {'end': 25738.929, 'text': 'right?. what is the chance of the pack less than 250 grams?', 'start': 25735.848, 'duration': 3.081}], 'summary': 'Probability allows answering weight questions accurately. requesting data for <280g and calculating <250g pack probability.', 'duration': 23.026, 'max_score': 25715.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25715903.jpg'}, {'end': 25962.083, 'src': 'embed', 'start': 25900.611, 'weight': 0, 'content': [{'end': 25908.14, 'text': "if i go to plus minus 4.5 standard deviations, i'll be at around 3.4 into the power minus 6 to reach that for the customer.", 'start': 25900.611, 'duration': 7.529}, {'end': 25912.405, 'text': 'i need to go to six sigma here, which is about one in a billion.', 'start': 25908.16, 'duration': 4.245}, {'end': 25915.508, 'text': 'i must be more accurate in my factory floor.', 'start': 25913.306, 'duration': 2.202}, {'end': 25917.159, 'text': 'for my customer.', 'start': 25916.459, 'duration': 0.7}, {'end': 25927.565, 'text': 'so if i reach six sigma my customer will reach 4.5 sigma and for customer 4.5 sigma is the 3.4 into the power minus 6.', 'start': 25918, 'duration': 9.565}, {'end': 25930.887, 'text': "so if you look at 3.4 into the power minus 6, it doesn't correspond to six sigma.", 'start': 25927.565, 'duration': 3.322}, {'end': 25936.91, 'text': "little confusing but that's the way six sigma literature is written.", 'start': 25933.028, 'duration': 3.882}, {'end': 25940.872, 'text': 'the normal distribution is just this as a formula.', 'start': 25938.951, 'duration': 1.921}, {'end': 25954.617, 'text': 'plus plus 1 plus or minus 2 sigma is 95% actually actually plus or minus 1.96 sigma is 95% and 3 sigma is about 99.7% infinity.', 'start': 25942.005, 'duration': 12.612}, {'end': 25962.083, 'text': 'by definition goes to infinity you want to cover everything plus minus infinite standard deviation.', 'start': 25956.899, 'duration': 5.184}], 'summary': 'To achieve six sigma accuracy, reaching 3.4x10^-6 for customer, aiming for one in a billion quality on the factory floor.', 'duration': 61.472, 'max_score': 25900.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY25900611.jpg'}], 'start': 24677.654, 'title': 'Normal distribution and probability', 'summary': 'Covers topics such as estimating mean and standard deviation using normal distribution, probability calculations for cereal pack weight, advantages of normal distribution, data vs probability in analysis, hypothesis testing, and distributions in statistics. it also addresses misconceptions about six sigma.', 'chapters': [{'end': 24761.548, 'start': 24677.654, 'title': 'Understanding normal distribution', 'summary': 'Discusses how to estimate the mean and standard deviation of a normal distribution using the range and the concept of sigma, allowing for cheat calculations with minimal information.', 'duration': 83.894, 'highlights': ["The whole range of the distribution from 8 to 10 o'clock covers about six standard deviations, allowing for estimation of the mean and standard deviation without actual data.", 'The normal distribution is characterized by two parameters, mu and sigma, providing a way to calculate and understand the distribution.', 'The chapter emphasizes the cheat calculations with minimal information and the usefulness of visual representations in understanding distributions.']}, {'end': 25178.38, 'start': 24761.568, 'title': 'Probability calculation for cereal pack weight', 'summary': 'Discusses the probability calculations for a morning breakfast cereal pack weighing less than 280 grams, more than 350 grams, and between 260 grams and 340 grams, with a mean weight of 295 kilograms and a standard deviation of 0.25 kilograms, resulting in probabilities of 27%, 1.39%, and 88% respectively.', 'duration': 416.812, 'highlights': ['The probability of the cereal pack weighing less than 280 grams is 27%, calculated using the mean weight of 295 kilograms and a standard deviation of 0.25 kilograms.', 'The probability of the cereal pack weighing more than 350 grams is 1.39%, determined by subtracting the cumulative distribution function (CDF) value for 350 grams from 1.', '88% of the cereal packs are expected to weigh between 260 grams and 340 grams, based on the given mean weight and standard deviation.']}, {'end': 25338.611, 'start': 25178.44, 'title': 'Advantages of normal distribution', 'summary': 'Discusses the advantages of using the normal distribution, including its application in certain cases, the central limit theorem, and the assumption of normality in the absence of other data information.', 'duration': 160.171, 'highlights': ['The central limit theorem states that the averages or totals of things result in a normal distribution, making it a good assumption for observations that are totals of the accumulation of many things.', 'The normal distribution is often used as an assumption based on the central limit theorem, especially in cases where the observation is the total accumulation of many things, such as height, which is often normal due to being a random combination of many factors.', "The assumption of normality is often made in the absence of any other information on the data, even if the data doesn't look like a normal distribution, but it is obviously wrong in cases where the data has a very strong skew.", 'In certain cases, such as when looking at lifetimes of things, the advantage of the normal distribution is evident, but there are also other distributions that can be used based on what makes most sense for the application.']}, {'end': 25580.075, 'start': 25338.631, 'title': 'Data vs probability in analysis', 'summary': 'Discusses the confusion between data and probability questions in analysis, emphasizing the importance of using data to derive mean and standard deviation for making business decisions, while highlighting the lack of necessity of data in certain situational and probability questions.', 'duration': 241.444, 'highlights': ['Using data to derive mean and standard deviation for making business decisions The need for mean and standard deviation to be derived from data in order to answer business questions such as product purchase likelihood, network reliability, and product quality.', 'Emphasizing the lack of necessity of data in certain situational and probability questions The author argues that certain questions, such as determining the weight of a pack or the reaction of an autonomous car, may not require extensive data analysis and can be addressed independently of data.', 'The confusion between data and probability questions in analysis The chapter addresses the confusion between data-related and probability-related questions, highlighting the importance of discerning the type of question being asked in analytical scenarios.']}, {'end': 25761.753, 'start': 25582.016, 'title': 'Hypothesis testing and probability in statistics', 'summary': 'Discusses the importance of calculating means and standard deviations in hypothesis testing, the impact of normal distribution on reliability of estimations, and the power of probability in answering questions without empirical data.', 'duration': 179.737, 'highlights': ['The chapter emphasizes the significance of calculating means and standard deviations in hypothesis testing. The discussion revolves around the importance of calculating means and standard deviations from data to solve problems through hypothesis testing.', "The impact of normal distribution on the reliability of estimations is explained. The calculation's reliance on the normality of future data and the use of specific formulas for normal distributions are discussed, highlighting its importance in determining the reliability of estimations.", 'The power of probability in answering questions without empirical data is emphasized. The ability to answer questions like the likelihood of the weight being less than 280 grams without empirical data is highlighted, showcasing the power of probability in statistical analysis.']}, {'end': 25962.083, 'start': 25762.093, 'title': 'Distributions and six sigma', 'summary': 'Discusses the use of distributions to obtain numerical values and clarifies the confusion around the definition of six sigma, indicating that 4.5 sigma corresponds to 3.4 defects per million opportunities, not 6 sigma.', 'duration': 199.99, 'highlights': ['The normal distribution and its use in obtaining numerical values are explained in the context of computer program bugs and malicious attempts on servers.', 'The confusion surrounding the definition of Six Sigma is addressed, highlighting that 4.5 sigma corresponds to 3.4 defects per million opportunities, not 6 sigma.', 'The concept of sigma levels in Six Sigma is discussed, emphasizing that reaching six sigma equates to about one in a billion defects per million opportunities.']}], 'duration': 1284.429, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/Vfo5le26IhY/pics/Vfo5le26IhY24677654.jpg', 'highlights': ['The central limit theorem states that the averages or totals of things result in a normal distribution, making it a good assumption for observations that are totals of the accumulation of many things.', 'The normal distribution is often used as an assumption based on the central limit theorem, especially in cases where the observation is the total accumulation of many things, such as height, which is often normal due to being a random combination of many factors.', "The assumption of normality is often made in the absence of any other information on the data, even if the data doesn't look like a normal distribution, but it is obviously wrong in cases where the data has a very strong skew.", 'The probability of the cereal pack weighing less than 280 grams is 27%, calculated using the mean weight of 295 kilograms and a standard deviation of 0.25 kilograms.', 'The probability of the cereal pack weighing more than 350 grams is 1.39%, determined by subtracting the cumulative distribution function (CDF) value for 350 grams from 1.', '88% of the cereal packs are expected to weigh between 260 grams and 340 grams, based on the given mean weight and standard deviation.', "The whole range of the distribution from 8 to 10 o'clock covers about six standard deviations, allowing for estimation of the mean and standard deviation without actual data.", 'The normal distribution and its use in obtaining numerical values are explained in the context of computer program bugs and malicious attempts on servers.', 'The confusion between data and probability questions in analysis The chapter addresses the confusion between data-related and probability-related questions, highlighting the importance of discerning the type of question being asked in analytical scenarios.', 'The concept of sigma levels in Six Sigma is discussed, emphasizing that reaching six sigma equates to about one in a billion defects per million opportunities.', 'The chapter emphasizes the significance of calculating means and standard deviations in hypothesis testing. The discussion revolves around the importance of calculating means and standard deviations from data to solve problems through hypothesis testing.']}], 'highlights': ["Dr. Abhinanda Sarkar's session in Great Learning's Business Analytics and Business Intelligence course has been ranked as the number one analytics program for the past four years.", 'The average salary for data science and machine learning jobs is $120,000 per year, making it one of the top five jobs globally, according to LinkedIn.', 'Dr. Abhinanda Sarkar, a Stanford University PhD holder, presents a comprehensive course in statistics for data science, emphasizing its importance in making predictions.', 'The value of asking the right question in data analysis In the world of statistics, the question is cheap and the data is expensive, highlighting the importance of asking the right questions for effective problem-solving.', 'Descriptive, predictive, and prescriptive analysis is crucial for understanding and addressing declining sales.', "The tutorial by Dr. Abhinanda Sarkar will be available on Great Learning's YouTube channel for a limited period to provide access to high-quality content for learners worldwide.", 'The distribution of the dataset is illustrated through age ranges and corresponding proportions, demonstrating variability.', 'The distinction between median and mean is clarified, with the median representing the age of the average person and the mean indicating the average age of a person within the dataset.', 'Standard deviation quantifies data dispersion from the mean.', 'Heat maps visually display correlations in large datasets, aiding analysis.', 'The process of test validate train involves holding aside a portion of available data as validation data, building the algorithm on the remaining data, and testing it on the held-out data to assess generalizability.', 'The joint probability of events is of interest, such as the probability of a customer being an IT professional and buying the product, to calculate another event, like the likelihood of selling a product to an IT professional.', 'The chapter challenges the perception of risk by discussing the probability calculation of HIV given a positive test, emphasizing the importance of understanding probability in decision-making.', 'The central limit theorem states that the averages or totals of things result in a normal distribution, making it a good assumption for observations that are totals of the accumulation of many things.']}